Artificial intelligence (AI) Books

Book SynopsisJo Bac’s groundbreaking legal study asks why and how the United States legal system should grant legal personhood to artificial intelligence (AI). This new legal status of AI is visualized as a dependent person, and the AI dependent legal person would be determined by an inextricable connection between AI and a new type of corporate body, introduced here as “AI-Human Amalgamation” (AI-HA).Artificial Intelligence has been defined as one or more computer programs with an ability to create work that is unforeseen by humans. This includes AI capacity to generate unforeseen innovations, patentable inventions, and/or infringe the rights of other patent holders. At present, AI is an entity unrecognized by law. The fact that AI is neither a natural nor a legal person indicates that it cannot be considered the owner of rights or bearer of liabilities. This in turn creates tension both in society and legal systems because questions such as who should hold the rights of AI or be liable for autonomous acts of AI remain unanswered.This book dynamically argues that the AI dependent legal person and AI-HA are necessary to address these new challenges. The creativity and actions of AI and AI-HA would be distinct from those performed by human beings involved in the creation of this amalgamation, such as AI’s operators or programmers. As such, this structure would constitute an amalgamation based on human beings and AI cooperation (AI-HA). As a dependent legal person, AI would hold the patent rights to its own inventions, thus ensuring favorable conditions for the incentives of the U.S. patent system. In addition, the proposed legal framework with the use of legislative instruments could address any liability concerns arising from foreseen and unforeseen actions, omissions, and AI’s failure to act.

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Book SynopsisChatbots such as ChatGPT are based on language models developed by using AI techniques. They are based on the Generative Pre-training Transformer (GPT) architecture and are trained on a huge amount of text data. This book edition covers different topics from chatbots and text generation, including: chatbots functioning, chatbot applications, implementation of text generation models and text generation applications.Table of Contents Section 1 Chatbot Functioning Chapter 1 Development of an E-Commerce Chatbot for a University Shopping Mall Chapter 2 Factors Affecting Consumers Adoption of AI-Based Chatbots: The Role of Anthropomorphism Chapter 3 A Novel Framework for Arabic Dialect Chatbot Using Machine Learning Chapter 4 Framework for Educational Domain-Based Multichatbot Communication System Chapter 5 Emerging Technologies of Natural Language-Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics Section 2 Chatbot Functioning Chapter 6 A Multi-Industry Analysis of the Future Use of AI Chatbots Chapter 7 Vik: A Chatbot to Support Patients with Chronic Diseases Chapter 8 A Smart Chatbot for Interactive Management in Beta Thalassemia Patients Chapter 9 Multi-Chatbot or Single-Chatbot? The Effects of M-Commerce Chatbot Interface on Source Credibility, Social Presence, Trust, and Purchase Intention Chapter 10 Development of NLP-Integrated Intelligent Web System for E-Mental Health Section 3 Implementing Text Generation Chapter 11 Enhancing Text Generation Via Parse Tree Embedding Chapter 12 Research and Implementation of Text Generation Based on Text Augmentation and Knowledge Understanding Chapter 13 An Integrated Deep Generative Model for Text Classification and Generation Chapter 14 Rapid Text Retrieval and Analysis Supporting Latent Dirichlet Allocation Based on Probabilistic ModelsSection 4 Text Generation Applications Chapter 15 The Automatic Question Generation System for CET Chapter 16 Exploration of Cross-Modal Text Generation Methods in Smart Justice Chapter 17 Feature Extraction and Intelligent Text Generation of Digital Music

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Book SynopsisThe generative AI is especially powerful in several areas, such as text generation (product description, article writing), image and video generation (AI-generated pictures and videos for marketing industry), and voice and sound generation (for film industry). This book edition covers different topics of generative AI models, including: image generation techniques, video generation techniques, speech / voice generation techniques, and societal and ethical issues of these models.Table of ContentsSection 1 Image Generation TechniquesChapter 1 Research on Image Generation and Style Transfer Algorithm Based on Deep LearningChapter 2 An Overview of Image Caption Generation MethodsChapter 3 Application of an Improved DCGAN for Image GenerationChapter 4 Private Face Image Generation Method Based on Deidentification in Low LightChapter 5 Application of Remote Sensing Image Data Scene Generation Method in Smart CitySection 2 Video Generation TechniquesChapter 6 Realistic Speech-Driven Talking Video Generation with Personalized PoseChapter 7 Video Transformation in Big Video Era and its Impact on Content EditingChapter 8 A Fast Depth-Map Generation Algorithm Based on Motion Search from 2D Video ContentsChapter 9 Adaptive Content Management for UGC Video Delivery in Mobile Internet EraSection 3 Voice and Speech GenerationChapter 10 Generating the Voice of the Interactive Virtual AssistantChapter 11 Voice Quality Modelling for Expressive Speech SynthesisChapter 12 Prosodically Rich Speech Synthesis Interface Using Limited Data of Celebrity VoiceChapter 13 Resources for Development of Hindi Speech Synthesis System: An OverviewSection 4 Societal and Ethical IssuesChapter 14 How AI-Human Symbiotes May Reinvent Innovation and What the New Centaurs Will Mean for CitiesChapter 15 AI, Automation and New JobsChapter 16 Discussion on the Development of Artificial Intelligence in TaxationChapter 17 AI and Zen: AI Films as Reflections on Reality and IllusionChapter 18 Ecologically Sound Procedural Generation of Natural Environments

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Book SynopsisHow do we understand the world around us? How do we solve problems? Often the answer to these questions follows a certain pattern, an algorithm if you wish. This is the case when our analytical left-brain side is at work. However, there are also elements in our behaviour where intelligence appears to follow a more elusive path, which cannot easily be characterised as a specific sequence of steps. Is Intelligence an Algorithm? offers an insight into intelligence as it functions in nature, like human or animal intelligence, but also sheds light on modern developments in the field of artificial intelligence, proposing further architectural solutions for the creation of a so-called global Webmind.

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Book SynopsisCommitted to highlighting the regulatory needs and priorities of emerging economies in the context of AI and big data, this expertly crafted Companion explores the nature and role of regulation in the Global South from a techno-dependent societal perspective. It not only amplifies the unspoken and underrepresented voices in AI and data regulation scholarly discourse, but also provides a novel approach to otherwise recipient economies in an age of digital transformation.Covering central themes such as regulatory flows, self-regulation and AI ethics, contextual regulation, and regulatory devices, the Companion brings together an array of eminent academics from across the globe. Chapters critically reflect on the nature and role of regulation, charting the tapestry of regulatory influence and capacity, values, and relationships of dependence and vulnerability attendant on advancing AI and mass data sharing. The regulatory challenges facing emerging economies and post-colonial societies are examined, and contributors engage new frames of thinking and solutions from perspectives beyond the interests of techno-colonialism.International and interdisciplinary in scope, this Companion will be an interesting read for academics and students in development studies, law and development, innovation and technology studies, and regulation and governance.Table of ContentsContents : Introduction to the Elgar Companion to Regulating AI and Big Data in Emergent Economies 1 Mark Findlay, Li Min Ong and Wenxi Zhang PART I EDITORS’ REFLECTIONS: REGULATORY FLOWS 1 The ongoing AI-regulation debate in the EU and its influence on the emergent economies – a new case for the ‘Brussels Effect’? 22 Shu Li, Béatrice Schütte and Suvi Sankari 2 Challenges and opportunities of ethical AI and digital technology use in emerging economies 42 Meera Sarma, Chaminda Senaratne and Thomas Matheus 3 Private-public data governance in Indonesia’s smart cities: promises and pitfalls 59 Berenika Drazewska PART II EDITORS’ REFLECTIONS: SELF-REGULATION AND AI ETHICS 4 The challenges of industry self-regulation of AI in emerging economies: implications of the case of Russia for public policy and institutional development 81 Gleb Papyshev and Masaru Yarime 5 The place of the African relational and moral theory of Ubuntu in the global artificial intelligence and big data discussion: critical reflections 99 Beatrice Okyere-Manu 6 The values of an AI ethical framework for a developing nation: considerations for Malaysia 115 Jaspal Kaur Sadhu Singh PART III EDITORS’ REFLECTIONS: CONTEXTUAL REGULATION 7 The relevance of culture in regulating AI and big data: the experience of the Macao SAR 138 Sara Migliorini and Rostam J. Neuwirth 8 Digital self-determination: an alternative paradigm for emerging economies 158 Wenxi Zhang, Li Min Ong and Mark Findlay PART IV EDITORS’ REFLECTIONS: REGULATORY DEVICES 9 Regulating AI in democratic erosion: context, imaginaries and voices in the Brazilian debate 183 Clara Iglesias Keller and João Carlos Magalhães 10 The importance and challenges of developing a regulatory agenda for AI in Latin America 201 Armando Guio Español, María Antonia Carvajal, Elena Tamayo Uribe and María Isabel Mejía 11 Artificial intelligence: dependency, coloniality and technological subordination in Brazil 228 Joyce Souza and Rodolfo Avelino Conclusion: reflecting on the ‘new’ North/South 245 Mark Findlay, Li Min Ong and Wenxi Zhang Index 259

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Book SynopsisDigital technologies are reshaping every field of social and economic lives, so do they in the world of scientific knowledge. “The New Challenges of Knowledge” aims at understanding how the new digital technologies alter the production, diffusion and valorization of knowledge. We propose to give an insight into the economical, geopolitical and political stakes of numeric in knowledge in different countries. Law is at the center of this evolution, especially in the case of national and international confusion about Internet, Science and knowledge.Trade Review“Sharing economy models are rippling through the world of scientific knowledge and research; open access brings challenges for developers, researchers, and policy makers – all treated here in the context of law-making” The Magpi, issue 60, Aug 2017Table of ContentsIntroduction . xiii Part 1. Production: Global Knowledge and Science in the Digital Era 1 Chapter 1. Current Knowledge Dynamics 3 1.1. Transparency of scientific data 4 1.2. Transparency of experimental protocol 6 1.3. A necessary form of research engineering 7 1.4. Confusion between data and scientific results: avoiding manipulation of research results 8 Chapter 2. Digital Conditions for Knowledge Production 11 2.1. An economic system oriented toward innovation 11 2.2. What of knowledge and indeed the concept of the commons? 13 2.3. From analog to digital 14 2.4. User–producer: civil society enters the knowledge production system 16 2.5. The interactions between the various spheres of knowledge production 18 2.6. Collaboration between society and knowledge: producing authorities should be put into perspective 20 Chapter 3. The Dual Relationship between the User and the Developer 23 3.1. Legal arrangements for knowledge-sharing using development platforms 23 3.2. The user contributes to the creation and development of content process 25 Chapter 4. Researchers’ Uses and Needs for Scientific and Technical Information 29 4.1. The CNRS survey 29 4.2. Diverse uses and dual needs 31 4.3. An explanation through differentiated scientific analysis 33 Chapter 5. New Tools for Knowledge Capture 37 5.1. The growth of metadata exploitation 37 5.2. Are we moving toward a semantic Web? 38 5.3. Tools and limits for metadata processing 39 5.4. The challenges of the semantic Web 40 Chapter 6. Modes of Knowledge Sharing and Technologies 43 6.1. Data storage technologies and access allowing knowledge sharing 43 6.2. Exchange platforms and catalogs 44 6.3. Knowledge-processing and digital editions 45 Part 2. Sharing Mechanisms: Knowledge Sharing and the Knowledge-based Economy 47 Chapter 7. Business Model for Scientific Publication 49 7.1. The current economic model is changing so as to adapt to new conditions for knowledge sharing 49 7.2. Creation of a new model 51 7.3. The issues raised by the creation of a new economic model 52 7.4. A new economic model struggling to fine its niche 54 Chapter 8. Actor Strategy: International Scientific Publishing, Services with High Added Value and Research Communities 57 8.1. Publishing, editing and existing: live issues within the publication of Scientific and Technical Information (STI) 58 8.2. Who is subject to it? The other players in scientific publishing 59 8.3. The characteristics of SMS (Science of Man and Society) 60 8.4. Existing without publishing? New STI directions 62 8.5. Alternatives to scientific publishing 63 Chapter 9. New Approaches to Scientific Production 67 9.1. New means of access to scientific production: innovative models 67 9.2. Two main objectives: accelerating knowledge sharing and promoting scientific collaboration 71 9.3. The need for new analytical tools and the risk of reprivatization of scientific knowledge. 72 9.4. The absence of the usage doctrine and the risk of reprivatization of science: the case of social networks 74 Chapter 10. The Geopolitics of Science 77 10.1. National convergent research models 78 10.2. Science is a source of international cooperation 81 10.3. International scientific cooperation is accelerating 84 Chapter 11. Copyright Serving the Market 85 Part 3. Enhancement Knowledge Rights and Public Policies in the Wake of Digital Technology 89 Chapter 12. Legal Protection of Scientific Research Results in the Humanities and Social Sciences 91 12.1.Different legal protections for different kinds of science 91 12.2. Why protect? 92 12.3. How to protect 93 12.4. Protect against whom? 98 12.5. Changing the challenges of Internet protection 99 12.6. Legal obstacles related to the author’s right 100 Chapter 13. Development of Knowledge and Public Policies 103 13.1. Knowledge enhancement concerns everyone 104 13.2. What are the public policies for enhancing knowledge? 105 13.3. State establishment of connections between actors: a key tool in knowledge enhancement 107 13.4. Comparing the United States and the European Union 109 Chapter 14. From Author to Enhancer 111 14.1. Enhancing scientific research is a complex process 112 14.2. Scientific research enhancement follows a legislative framework intended to promote innovation 114 Chapter 15. The Right to Knowledge: Moving Toward a Universal Law? 117 15.1. Unclear regulatory frameworks 118 15.2. Developing legal frameworks related to the Internet is complicated 121 15.3. Proposals for developing legal frameworks for the Internet 123 Chapter 16. Governing by Algorithm 127 16.1. Statistics that foreshadow algorithms 128 16.2. Algorithmic governance and democratic opportunities 130 Chapter 17. Public Data and Science in e-Government 133 17.1. Disseminating data and disseminating science: a new requirement 134 17.2. Public data in the e-government 137 17.3. Science within e-government 139 Chapter 18. Surveillance, Sousveillance, Improper Capturing 141 18.1. The traditional legal framework for information capture 142 18.2. The clear need for a specific law 145 Chapter 19. Public Knowledge Policies in the Digital Age 149 19.1. GAFA domination and the oligopolization of the market 150 19.2. Isolated digital ecosystems 152 19.3. Regulation through competition law 153 19.4. Data protection: moving toward a law for the digital community 154 Chapter 20. The Politics of Creating Artificial Intelligence 157 20.1. History 158 20.2. Artificial intelligence has become a priority for public and private actors 160 20.4. The appearance of legal problems 162 Chapter 21. Security Policies in Artificial Intelligence 165 21.1. Security as a comment on machines and data 166 21.2. From the security of machines to the security of humans 169 Conclusion 175 Postscript 177 Glossary 179 Bibliography 185 Index 201

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Book SynopsisVirtual and Augmented Reality have existed for a long time but were stuck to the research world or to some large manufacturing companies. With the appearance of low-cost devices, it is expected a number of new applications, including for the general audience. This book aims at making a statement about those novelties as well as distinguishing them from the complexes challenges they raise by proposing real use cases, replacing those recent evolutions through the VR/AR dynamic and by providing some perspective for the years to come.Table of ContentsPreface xi Introduction xvBruno ARNALDI, Pascal GUITTON and Guillaume MOREAU Chapter 1. New Applications 1Bruno ARNALDI, Stéphane COTIN, Nadine COUTURE, Jean-Louis DAUTIN, Valérie GOURANTON, François GRUSON and Domitile LOURDEAUX 1.1. New industrial applications 1 1.1.1. Virtual reality in industry 1 1.1.2. Augmented reality and industrial applications 3 1.1.3. VR-AR for industrial renewal 4 1.1.4. And what about augmented reality? 12 1.2. Computer-assisted surgery 14 1.2.1. Introduction 14 1.2.2. Virtual reality and simulation for learning 16 1.2.3. Augmented reality and intervention planning 21 1.2.4. Augmented reality in surgery 26 1.2.5. Current conditions and future prospects 31 1.3. Sustainable cities 32 1.3.1. Mobility aids in an urban environment 33 1.3.2. Building and architecture 37 1.3.3. Cities and urbanism 41 1.3.4. Towards sustainable urban systems 46 1.4. Innovative, integrative and adaptive societies 48 1.4.1. Education 48 1.4.2. Arts and cultural heritage 54 1.4.3. Conclusion 60 1.5. Bibliography 61 Chapter 2. The Democratization of VR-AR 73Sébastien KUNTZ, Richard KULPA and Jérôme ROYAN 2.1. New equipment 73 2.1.1. Introduction 73 2.1.2. Positioning and orientation devices 74 2.1.3. Restitution devices 82 2.1.4. Technological challenges and perspectives 100 2.1.5. Conclusions on new equipment 109 2.2. New software 111 2.2.1. Introduction 111 2.2.2. Developing 3D applications 113 2.2.3. Managing peripheral devices 116 2.2.4. Dedicated VR-AR software solutions 119 2.2.5. Conclusion 120 2.3. Bibliography 121 Chapter 3. Complexity and Scientific Challenges 123Ferran ARGELAGUET SANZ, Bruno ARNALDI, Jean-Marie BURKHARDT, Géry CASIEZ, Stéphane DONIKIAN, Florian GOSSELIN, Xavier GRANIER, Patrick LE CALLET, Vincent LEPETIT, Maud MARCHAL, Guillaume MOREAU, Jérôme PERRET and Toinon VIGIER 3.1. Introduction: complexity 123 3.1.1. Physical model and detecting collisions 124 3.1.2. Populating 3D environments: single virtual human to a surging crowd 130 3.1.3. The difficulty of making 3D interaction natural 137 3.1.4. The difficulty of synthesizing haptic feedback 141 3.2. The real–virtual relationship in augmented reality 150 3.2.1. Acquisition and restitution equipment 151 3.2.2. Pose computation 152 3.2.3. Realistic rendering 156 3.3. Complexity and scientific challenges of 3D interaction 158 3.3.1. Introduction 158 3.3.2. Complexity and challenges surrounding the 3D interaction loop 158 3.3.3. Challenge 1: sensory-motor actions for interaction 159 3.3.4. Challenge 2: multisensory feedback 163 3.3.5. Challenge 3: users and perception 166 3.3.6. Conclusion 167 3.4. Visual perception 168 3.4.1. A glossary of terms related to unease, fatigue and physical discomfort 168 3.4.2. Display factors 173 3.4.3. Conclusion 179 3.5. Evaluation 179 3.5.1. Objectives and scope of this section 179 3.5.2. Evaluation: a complex problem 180 3.5.3. Evaluation using studies with human subjects 184 3.5.4. Drawbacks to overcome 193 3.5.5. Evolutions in measuring performance and behavior, characterizing participants 195 3.5.6. Conclusion and perspectives 200 3.6. Bibliography 201 Chapter 4. Towards VE that are More Closely Related to the Real World 217Géry CASIEZ, Xavier GRANIER, Martin HACHET, Vincent LEPETIT, Guillaume MOREAU and Olivier NANNIPIERI 4.1. “Tough” scientific challenges for AR 218 4.1.1. Choosing a display device . 218 4.1.2. Spatial localization 221 4.2. Topics in AR that are rarely or never approached 223 4.2.1. Introduction 223 4.2.2. Hybridization through a screen or HMD 224 4.3. Spatial augmented reality 227 4.3.1. Hybridization of the real world and the virtual world 227 4.3.2. Current evolutions 228 4.4. Presence in augmented reality . 229 4.4.1. Is presence in reality the model for presence in virtual environments? 229 4.4.2. Mixed reality: an end to the real versus virtual binary? 231 4.4.3. From mixed reality to mixed presence 231 4.4.4. Augmented reality: a total environment 232 4.5. 3D interaction on tactile surfaces 233 4.5.1. 3D interaction 234 4.5.2. 3D interaction on tactile surfaces 236 4.6. Bibliography 240 Chapter 5. Scientific and Technical Prospects 247Caroline BAILLARD, Philippe GUILLOTEL, Anatole LÉCUYER, Fabien LOTTE, Nicolas MOLLET, Jean-Marie NORMAND and Gaël SEYDOUX 5.1. The promised revolution in the field of entertainment 247 5.1.1. Introduction 247 5.1.2. Defining a new, polymorphic immersive medium 248 5.1.3. Promised experiences 251 5.1.4. Prospects 255 5.2. Brain-computer interfaces 258 5.2.1. Brain-computer interfaces: introduction and definitions 258 5.2.2. What BCIs cannot do 260 5.2.3. Working principle of BCIs . 261 5.2.4. Current applications of BCIs 263 5.2.5. The future of BCIs 268 5.3. Alternative perceptions in virtual reality 269 5.3.1. Introduction 269 5.3.2. Pseudo-sensory feedback 271 5.3.3. Alternative perception of movement 275 5.3.4. Altered perception of one’s body 278 5.3.5. Conclusion 283 5.4. Bibliography 284 Chapter 6. The Challenges and Risks of Democratization of VR-AR 289Philippe FUCHS 6.1. Introduction 289 6.2. Health and comfort problems 292 6.2.1. The different problems 292 6.2.2. Sensorimotor incoherences . 293 6.3. Solutions to avoid discomfort and unease 297 6.3.1. Presentation of the process . 297 6.3.2. Mitigation of the impact on visuo-vestibular incoherence 297 6.3.3. Removing visuo-vestibular incoherence by modifying the functioning of the interaction paradigm 298 6.3.4. Removing visuo-vestibular incoherence by modifying interfaces 299 6.3.5. Levels of difficulty in adapting 299 6.4. Conclusion 300 6.5. Bibliography 301 Conclusion 303Bruno ARNALDI, Pascal GUITTON and Guillaume MOREAU Postface 309Bruno ARNALDI, Pascal GUITTON and Guillaume MOREAU Glossary 315 List of Authors 317 Index 321

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Book SynopsisThe book is a monograph in the cross disciplinary area of Computational Intelligence in Finance and elucidates a collection of practical and strategic Portfolio Optimization models in Finance, that employ Metaheuristics for their effective solutions and demonstrates the results using MATLAB implementations, over live portfolios invested across global stock universes. The book has been structured in such a way that, even novices in finance or metaheuristics should be able to comprehend and work on the hybrid models discussed in the book.Table of Contents1. A Brief Primer on Metaheuristics. 2. Heuristic Portfolio Selection. 3. Risk Budgeted Portfolio Optimization. 4. Heuristic Optimization of Equity Market Neutral Portfolios. 5. Metaheuristic 130-30 Portfolio Construction. 6. Metaheuristic Portfolio Rebalancing with Transaction Costs.

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Book SynopsisThis book will present a complete modeling of the human psychic system that allows to generate the thoughts in a strictly organizational approach that mixes a rising and falling approach. The model will present the architecture of the psychic system that can generate sensations and thoughts, showing how one can feel thoughts. The model developed into an organizational architecture based on massive multiagent systems. The architecture will be fully developed, showing how an artificial system can be endowed with consciousness and intentionally generate thoughts and, especially, feel them. These results are multidisciplinary, combining both psychology and computer science disciplines.Table of ContentsTable of Definitions vii Introduction ix Chapter 1. The Organizational Architecture of the Psychic System and the Feeling of Thinking 1 1.1. The problem of the study of thought 2 1.2. The interpretation of neuronal aggregates 5 1.3. The function of the architecture of the Freudian model 7 1.4. The specific characteristics of the components of the system using a constructivist approach 14 1.5. The systemic layer and the regulators 26 1.6. The mental landscape 35 1.7. The feeling of thinking and the general organizational principle 45 1.8. The aim and the space of the regulators 55 1.9. The attractors 67 1.10. The generation of a representation 74 1.11. Unification between regulators and neuronal aggregates: the morphological model of the generating forms 78 1.12. The morphological and semantic conformation of the psychic system 86 1.13. The processing component of the visual sense with generating forms 91 1.14. The decisive intention to think 97 1.15. Linguistic capacity in the human conscious 101 1.16. An assessment of the functioning of the human psychic system 109 Chapter 2. The Computer Representation of an Artificial Consciousness 113 2.1. A multiagent design to generate an artificial psychic system 114 2.2. Designing the artificial psychic system using a multiagent approach 122 2.3. Self-control of the artificial psychic system using regulator agents 128 2.4. The organizational architecture of the system 133 2.5. Organizational memory and artificial experience 142 2.6. Affective and tendential states of the system 154 2.7. The production of representations and the sensation of thinking 161 2.7.1. Algorithm for the intentional production of a series of representations around a specific theme 163 2.8. The feeling of existing 176 2.9. The representation of the things and the apprehension of temporality 181 2.10. Multisystem deployment 186 2.11. The final fate of systems endowed with artificial consciousness 194 Conclusion 197 Bibliography 201 Index 205

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Book SynopsisThe field of artificial intelligence has made tremendous advances in the last few decades, but as smart as AI is now, it is getting exponentially smarter and becoming more autonomous in its actions. This raises a host of challenges to current legal doctrine, including whether the output of AI entities should count as 'speech', the extent to which AI should be regulated under antitrust and criminal law statutes, and whether AI should be considered an independent agent and responsible for its actions under the law of tort or agency. Containing chapters written by leading U.S., EU, and International law scholars, the Research Handbook presents current law, statutes, and regulations on the role of law in an age of increasingly smart AI, addressing issues of law that are critical to the evolution of AI and its role in society. To provide a broad coverage of the topic, the Research Handbook draws upon free speech doctrine, criminal law, issues of data protection and privacy, legal rights for increasingly smart AI systems, and a discussion of jurisdiction for AI entities that will not be 'content' to stay within the geographical boundaries of any nation state or be tied to a particular physical location. Using numerous examples and case studies, the chapter authors discuss the political and jurisdictional decisions that will have to be made as AI proliferates into society and transforms our government and social institutions. The Research Handbook will also introduce designers of artificially intelligent systems to the legal issues that apply to the make-up and use of AI from the technologies, algorithms, and analytical techniques. This essential guide to the U.S., EU, and other International law, regulations, and statutes which apply to the emerging field of 'law and AI' will be a valuable reference for scholars and students interested in information and intellectual property law, privacy, and data protection as well as to legal theorists and social scientists who write about the future direction and implications of AI. The Research Handbook will also serve as an important reference for legal practitioners in different jurisdictions who may litigate disputes involving AI, and to computer scientists and engineers actively involved in the design and use of the next generation of AI systems.Contributors include: W. Barfield, S. Bayern, S.J. Blodgett-Ford, R.G.A. Bone, T. Burri, A. Chin, J.A. Cubert, M. de Cock Buning, S. De Conca, S-.A. Elvy, A. Ezrachi, R. Leenes, Y. Lev-Aretz, A.R. Lodder, R.P. Loui, T.M. Massaro, L.T. McCarty, J.O. McGinnis, F. Moslein, H. Norton, N. Packin, U. Pagallo, S. Quattrocolo, W. Samore, F. Shimpo, M.E. Stucke, R. van den Hoven van Genderen, L. Vertinsky, A. von Ungern-Sternberg, J.F. Weaver, Y-.H. Weng, I. WildhaberTable of ContentsContents: Forward: Curtis E. A. Karnow Part I Introduction to Law and Artificial Intelligence 1. Towards a Law of Artificial Intelligence Woodrow Barfield 2. Accelerating AI John O. McGinnis 3. Finding the Right Balance in Artificial Intelligence and Law L. Thorne McCarty 4. Learning Algorithms and Discrimination Nizan Packin and Yafit Lev-Aretz 5. The Principal Japanese AI and Robot Strategy and Research Toward Establishing Basic Principles Fumio Shimpo Part II Regulation of Artificial Intelligence 6. Artificial Intelligence and Private Law Shawn Bayern 7. Regulation of Artificial Intelligence John Frank Weaver 8. Legal Personhood in the Age of Artificially Intelligent Robots Robert van den Hoven van Genderen 9. Autonomous Driving: Regulatory Challenges Raised by Artificial Decision-Making and Tragic Choices Antje von Ungern-Sternberg Part III Fundamental Rights and Constitutional Law Issues 10. Artificial Intelligence and Privacy- AI Enters the House Through the Cloud Ronald Leenes and Silvia De Conca 11. Future Privacy: A Real Right to Privacy for Artificial Intelligence S. J. Blodgett-Ford 12. Artificial Intelligence and the First Amendment Toni M. Massaro and Helen Norton 13. Data Algorithms and Privacy in Surveillance: On Stages, Numbers, and the Human Factor Arno R. Lodder and Ronald P. Loui 14. The Impact of AI on Criminal Law, and its Twofold Procedures Ugo Pagallo and Serena Quattrocolo Patrt IV Intellectual Property 15. The Law of Artificial Intelligence Intellectual Property Jeremy A. Cubert and Richard G. A. Bone 16. Kinematically Abstract Claims in Surgical Robotics Patents Andrew Chin 17. Artificial Intelligence and the Patent System: Can a New Tool Render a Once Patentable Idea Obvious? William Samore 18. Thinking Machines and Patent Law Liza Vertinsky 19. Artificial Intelligence and the Creative Industry: New Challenges for the EU Paradigm for Art and Technology by Autonomous Creation Madeleine de Cock Buning Part V Applications of Artificial Intelligence 20. Free Movement of Algorithms: Artificially Intelligent Persons Conquer the European Union’s Internal Market Thomas Burri 21. The Artificially Intelligent Internet of Things and Article 2 of the Uniform Commercial Code Stacy-Ann Elvy 22. Artificial Intelligence and Robotics, the Workplace, and Workplace-Related Law Isabelle Wildhaber 23. Robotics Law 1.0: On Social System Design for Artificial Intelligence Yueh-Hsuan Weng 24. Antitrust, Algorithmic Pricing and Tacit Collusion Maurice E. Stucke and Ariel Ezrachi 25. Robots in the Boardroom: Artificial Intelligence and Corporate Law Florian Möslein Index

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Book SynopsisAutonomous vehicles have attracted a great deal of attention in the media, however there are some inconsistencies between the perception of autonomous vehicles’ capabilities and their actual functions. This book provides an accessible explanation of how autonomous vehicles function, suggesting appropriate regulatory responses to the existing and emerging technology.Hannah YeeFen Lim explores the current capabilities of autonomous vehicles and importantly, highlights their inherent limitations. Lim provides a concise and easy to follow overview of the technology behind autonomous vehicles which encompasses hardware and software aspects, including machine learning algorithms. Having laid the technical foundation, the following chapters assess the current legal standards in negligence law that are applicable to autonomous vehicles taking the current technical limitations of the vehicles into account. Lim concludes by exploring the ethical issues associated with autonomous vehicles and proposes appropriate regulatory approaches. This book will be of great value to policy makers seeking a deeper understanding of the technology behind autonomous vehicles in order to inform and guide the development of laws and regulations. Legal practitioners will benefit from the discussion of recent use cases and applicable negligence law. Legal scholars researching artificial intelligence will also find the author’s easy to understand technical explanations and discourse on ethical considerations invaluable.Trade Review'Professor Lim's expertise in both law and computer science is evident in this clear and crisp assessment of liability issues surrounding Automated Vehicles (AV's). She demystifies the science and technology underlying this phenomenon that has captured the public imagination and left law and policy-makers scrambling. Transcending the hype around AVs, Professor Lim's thoughtful and tech-savvy application of negligence principles provides an essential framework through which the risks and benefits of AV technology can be more cogently assessed and addressed.' --Teresa Scassa, University of Ottawa, Canada'Self-driving cars are the vanguard of AI-based autonomous systems, machines which are about to transform our world. This book is a wonderful introduction and resource, both to the technology and the legal questions that we are facing. The author provides a clear how-to guide to regulating systems that are hard-to-understand but which, within a few years, will be piloting large pieces of metal at speed down roads that we used to have to ourselves. Recommended for anyone who thinks about what the future should look like.' --Dan Hunter, Swinburne Law School, AustraliaTable of ContentsContents 1. Introduction 2. How autonomous vehicles function 3. Verifiable Standards of Care 4. Software: Difficult to verify standards of care 5. The road less travelled for regulators 6. Ethical responsibilities and autonomous vehicles 7. For a smoother ride … Index

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Book SynopsisArtificial intelligence is creating huge opportunities for workplace learning and employee development. However, it can be difficult for L&D professionals to assess what difference AI can make in their organization and where it is best implemented. Artificial Intelligence for Learning is the practical guide L&D practitioners need to understand what AI is and how to use it to improve all aspects of learning in the workplace. It includes specific guidance on how AI can provide content curation and personalization to improve learner engagement, how it can be implemented to improve the efficiency of evaluation, assessment and reporting and how chatbots can provide learner support to a global workforce. Artificial Intelligence for Learning debunks the myths and cuts through the hype around AI allowing L&D practitioners to feel confident in their ability to critically assess where artificial intelligence can make a measurable difference and where it is worth investing in. There is also critical discussion of how AI is an aid to learning and development, not a replacement as well as how it can be used to boost the effectiveness of workplace learning, reduce drop off rates in online learning and improve ROI. With real-world examples from companies who have effectively implemented AI and seen the benefits as well as case studies from organizations including Netflix, British Airways and the NHS, this book is essential reading for all L&D practitioners needing to understand AI and what it means in practice.Trade Review"The world of workplace learning will be dominated by AI within a few years. Artificial Intelligence for Learning plots a clear and concise path through what is the biggest opportunity the industry has had for many years." * Paul McElvaney, CEO of Learning Pool *"Donald Clark has been at the leading edge of technology in learning for over 30 years. His take on tech is always informed by his detailed knowledge of learning theory. This book on AI is no exception - it's bold, thorough, bang up to date, well-researched, evidence-based and practical." * Kirstie Donnelly MBE, CEO of City & Guilds Group *Table of Contents Section - PART ONE: Introduction; Section - 01: Homo technus; Section - 02: What is AI?; Section - PART TWO: Teaching; Section - 03: Robot teacher fallacy; Section - 04: Teaching; Section - PART THREE: Chatbots; Section - 05: AI is the new UI; Section - 06: Chatbots; Section - 07: Building chatbots; Section - PART FOUR: Learning; Section - 08: Content creation; Section - 09: Video; Section - 10: Push learning; Section - 11: Adaptive learning; Section - 12: Learning organizations; Section - 13: Assessment; Section - PART FIVE: Data; Section - 14: Data analytics; Section - 15: Sentiment analysis; Section - PART SIX: Future; Section - 16: Future skills; Section - 17: Ethics and bias; Section - 18: Employment; Section - 19: The final frontier; Section - 20: Where next?; Section - 21: Index

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Book SynopsisLeading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI.Trade Review"After years of progress in AI, might the hype be growing faster than the reality? Are we about to enter an 'AI autumn'? Not if Borek and Prill have anything to say about it! Digital transformation is tough - this book improves your odds." * Thomas C Redman, "the Data Doc", Harvard Business Review Blogger and Author *"Clear and to the point in a language that works for executives. A must-read for any leader." * Holger Kömm, Senior Director Advanced Analytics, Adidas *"A great in-depth introduction to how to add value to companies using digitalization, data and AI." * Patrick Glauner, Professor of Artificial Intelligence, Deggendorf Institute of Technology *"Provides great guidance on how to think of data products instead of projects - which is a key factor in mastering the challenges of digitalization." * Carsten Bange, Founder and CEO, BARC (Business Application Research Centre) *"A must-read for everyone involved into turning digitization and AI into real value for your company. Whether you're in the middle of the process looking for some orientation or just about getting started, this book will provide you with the advice you need!" * Alexander Thamm, CEO and Founder of Alexander Thamm GmbH *Table of Contents Chapter - 01: Introduction to delivering data and AI products; Chapter - 02: Developing the data and AI product strategy and goals; Chapter - 03: Setting up the data and AI product delivery organization; Chapter - 04: Identifying and defining data and AI products; Chapter - 05: Delivering high quality data and AI products; Chapter - 06: Designing the data and AI platform and architecture; Chapter - 07: Driving transformative change with data and AI products; Chapter - 08: The future of data and AI products in your organization

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Book SynopsisLeading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI.Trade Review"After years of progress in AI, might the hype be growing faster than the reality? Are we about to enter an 'AI autumn'? Not if Borek and Prill have anything to say about it! Digital transformation is tough - this book improves your odds." * Thomas C Redman, "the Data Doc", Harvard Business Review Blogger and Author *"Clear and to the point in a language that works for executives. A must-read for any leader." * Holger Kömm, Senior Director Advanced Analytics, Adidas *"A great in-depth introduction to how to add value to companies using digitalization, data and AI." * Patrick Glauner, Professor of Artificial Intelligence, Deggendorf Institute of Technology *"Provides great guidance on how to think of data products instead of projects - which is a key factor in mastering the challenges of digitalization." * Carsten Bange, Founder and CEO, BARC (Business Application Research Centre) *"A must-read for everyone involved into turning digitization and AI into real value for your company. Whether you're in the middle of the process looking for some orientation or just about getting started, this book will provide you with the advice you need!" * Alexander Thamm, CEO and Founder of Alexander Thamm GmbH *Table of Contents Chapter - 01: Introduction to delivering data and AI products; Chapter - 02: Developing the data and AI product strategy and goals; Chapter - 03: Setting up the data and AI product delivery organization; Chapter - 04: Identifying and defining data and AI products; Chapter - 05: Delivering high quality data and AI products; Chapter - 06: Designing the data and AI platform and architecture; Chapter - 07: Driving transformative change with data and AI products; Chapter - 08: The future of data and AI products in your organization

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Book SynopsisElgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences and law, expertly written by the world's leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas. Woodrow Barfield and Ugo Pagallo present a succinct introduction to the legal issues related to the design and use of artificial intelligence (AI). Exploring human rights, constitutional law, data protection, criminal law, tort law, and intellectual property law, they consider the laws of a number of jurisdictions including the US, the European Union, Japan, and China, making reference to case law and statutes. Key features include: a critical insight into human rights and constitutional law issues which may be affected by the use of AI discussion of the concept of legal personhood and how the law might respond as AI evolves in intelligence an introduction to current laws and statutes which apply to AI and an identification of the areas where future challenges to the law may arise. This Advanced Introduction is ideal for law and social science students with an interest in how the law applies to AI. It also provides a useful entry point for legal practitioners seeking an understanding of this emerging field.Trade Review‘Barfield and Pagallo’s book offers a great overview on the most discussed and practically relevant legal discussions about AI. The authors portray the currently applicable laws and the relevant decisions comprehensibly for law students and non-lawyers. The references throughout the book as well as a list of additional topics will assist readers who would like to expand their knowledge. They present an overview and offer law students several carefully chosen gateways through which readers may explore the vast and steadily growing literature in the field. If you are looking for a concise book on the manifold issues of artificial intelligence and law, Barfield and Pagallo’s Advanced Introduction to Law and Artificial Intelligence is a great starting point.’ -- Carolin Kemper, Prometheus‘Edward Elgar has hit the nail on the head by choosing this particular topic to publish in its Edward Elgar Advanced Introduction Series. It is a much need book at this time when the hype about Artificial Intelligence (AI) is at a crescendo level.’ -- Sally Ramage, Criminal Lawyer‘This book provides an authoritative introduction into the specific legal topics covered, and a springboard into further research, and will prove a useful resource for its intended audience.’ -- Stephanie Falconer, Law in Context'A much needed comprehensive and up-to-date introduction to the law of AI, a must read for all ICT lawyers!' --Giovanni Sartor, University of Bologna and European University Institute, ItalyTable of ContentsContents: Introduction to Law and Artificial Intelligence 1. Definitions, Actors, Concepts 2. Human Rights Considerations 3. Constitutional Law Issues 4. Legal Personality and Artificial Intelligence 5. Issues of Data Protection 6. Tort Law Approaches 7. Criminal Law 8. Copyright Law 9. Patent Law 10. Business Law, Antitrust, and Trade Secrets 11. Looking Ahead: Towards a Law of Artificial Intelligence Index

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Book SynopsisGathering insightful and stimulating contributions from leading global experts in Artificial Intelligence in Education (AIED), this comprehensive Handbook traces the development of AIED from its early foundations in the 1970s to the present day. The Handbook evaluates the use of AI techniques such as modelling in closed and open domains, machine learning, analytics, language understanding and production to create systems aimed at helping learners, teachers, and educational administrators. Chapters examine theories of affect, metacognition and pedagogy applied in AIED systems; foundational aspects of AIED architecture, design, authoring and evaluation; and collaborative learning, the use of games and psychomotor learning. It concludes with a critical discussion of the wider context of Artificial Intelligence in Education, examining its commercialisation, social and political role, and the ethics of its systems, as well as reviewing the possible challenges and opportunities for AIED in the next 20 years. Providing a broad yet detailed account of the current field of Artificial Intelligence in Education, researchers and advanced students of education technology, innovation policy, and university management will benefit from this thought-provoking Handbook. Chapters will also be useful to support undergraduate courses in AI, computer science, and education.Trade Review‘The Handbook of Artificial Intelligence in Education is a great resource for people studying the field of AI and data-directed education, which is a complex tapestry, with many different elements feeding into its design and development. The Handbook supports readers to experience the full tapestry and to pull out and examine individual threads, without losing the underlying purpose. The field has grown tremendously in the past 30 years. Intelligent tutors now listen to and speak to learners, model student expertise and knowledge, examine theories (about how humans learn, think, collaborate and socialize), and support classroom orchestration, learning at scale, assessment, and human-machine interaction. The Handbook nicely documents progress in the field without overwhelming a new generation of AIED researchers.’ -- Beverly Woolf, author of Building Intelligent Interactive Tutors and University of Massachusetts, Amherst, US‘This is a really great Handbook written by some of the most well known authors in the field of Artificial Intelligence in Education (AIED). It describes both the most important topics and future trends in a very comprehensive way for a wide range of stakeholders.’ -- Cristóbal Romero, University of Córdoba, SpainTable of ContentsContents: Foreword xii PART I SCENE SETTING 1 Introduction 2 Benedict du Boulay, Antonija Mitrovic and Kalina Yacef 2 The history of artificial intelligence in education – the first quarter century 10 Gordon McCalla PART II THEORIES UNDERPINNING AIED 3 The role and function of theories in AIED 31 Stellan Ohlsson 4 Theories of metacognition and pedagogy applied to AIED systems 45 Roger Azevedo and Megan Wiedbusch 5 Theories of affect, meta-affect, and affective pedagogy 68 Ivon Arroyo, Kaśka Porayska-Pomsta and Kasia Muldner 6 Scrutable AIED 101 Judy Kay, Bob Kummerfeld, Cristina Conati, Kaśka Porayska-Pomsta and Ken Holstein PART III THE ARCHITECTURE AND DESIGN OF AIED SYSTEMS 7 Domain modeling for AIED systems with connections to modeling student knowledge: a review 127 Vincent Aleven, Jonathan Rowe, Yun Huang and Antonija Mitrovic 8 Student modeling in open-ended learning environments 170 Cristina Conati and Sébastien Lallé 9 Six instructional approaches supported in AIED systems 184 Vincent Aleven, Manolis Mavrikis, Bruce M. McLaren, Huy A. Nguyen, Jennifer Olsen and Nikol Rummel 10 Theory-driven design of AIED systems for enhanced interaction and problem-solving 229 Susanne Lajoie and Shan Li 11 Deeper learning through interactions with students in natural language 250 Vasile Rus, Andrew M. Olney and Arthur C. Graesser 12 Authoring tools to build AIED systems 273 Stephen Blessing, Stephen B. Gilbert and Steven Ritter PART IV ANALYTICS 13 Continuous student modeling for programming in the classroom: challenges, methods, and evaluation 287 Ye Mao, Samiha Marwan, Preya Shabrina, Yang Shi, Thomas W. Price, Min Chi and Tiffany Barnes 14 Human–AI co-orchestration: the role of artificial intelligence in orchestration 309 Ken Holstein and Jennifer Olsen 15 Using learning analytics to support teachers 322 Stanislav Pozdniakov, Roberto Martinez-Maldonado, Shaveen Singh, Hassan Khosravi and Dragan Gašević 16 Predictive modeling of student success 350 Christopher Brooks, Vitomir Kovanović and Quan Nguyen 17 Social analytics to support engagement with learning communities 370 Carolyn Rosé, Meredith Riggs and Nicole Barbaro PART V AIED SYSTEMS IN USE 18 Intelligent systems for psychomotor learning: A systematic review and two cases of study 390 Alberto Casas-Ortiz, Jon Echeverria and Olga C. Santos 19 Artificial intelligence techniques for supporting face-to-face and online collaborative learning 422 Roberto Martinez-Maldonado, Anouschka van Leeuwen and Zachari Swiecki 20 Digital learning games in artificial intelligence in education (AIED): a review 440 Bruce M. McLaren and Huy A. Nguyen 21 Artificial intelligence-based assessment in education 487 Ying Fang, Rod D. Roscoe and Danielle S. McNamara 22 Evaluations with AIEd systems 507 Kurt VanLehn 23 Large-scale commercialization of AI in school-based environments 526 Steven Ritter and Kenneth R. Koedinger 24 Small-scale commercialisation: the golden triangle of AI EdTech 539 Rosemary Luckin and Mutlu Cukurova 25 Critical perspectives on AI in education: political economy, discrimination, commercialization, governance and ethics 555 Ben Williamson, Rebecca Eynon, Jeremy Knox and Huw Davies 26 The ethics of AI in education 573 Kaśka Porayska-Pomsta, Wayne Holmes and Selena Nemorin PART VI THE FUTURE 27 The great challenges and opportunities of the next 20 years 608 1. AIED and equity 608 Maria Mercedes T. Rodrigo 2. Engaging learners in the age of information overload 610 Julita Vassileva 3. Pedagogical agents for all: designing virtual characters for inclusion and diversity in STEM 613 H. Chad Lane 4. Intelligent textbooks 616 Peter Brusilovsky and Sergey Sosnovsky 5. AI-empowered open-ended learning environments in STEM domains 620 Gautam Biswas 6. Ubiquitous-AIED: pervasive AI learning technologies 626 James C. Lester 7. Culture, ontology and learner modeling 629 Riichiro Mizoguchi 8. Crowdsourcing paves the way for personalized learning 632 Ethan Prihar and Neil Heffernan 9. AIED in developing countries: breaking seven WEIRD assumptions in the global learning XPRIZE field study 635 Jack Mostow 10. The future of learning assessment 639 Claude Frasson 11. Intelligent mentoring systems: tapping into AI to deliver the next generation of digital learning 642 Vania Dimitrova Index 653

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Book SynopsisThis provocative book investigates the relationship between law and artificial intelligence (AI) governance, and the need for new and innovative approaches to regulating AI and big data in ways that go beyond market concerns alone and look to sustainability and social good. Taking a multidisciplinary approach, the contributors demonstrate the interplay between various research methods, and policy motivations, to show that law-based regulation and governance of AI is vital to efforts at ensuring justice, trust in administrative and contractual processes, and inclusive social cohesion in our increasingly technologically-driven societies. The book provides valuable insights on the new challenges posed by a rapid reliance on AI and big data, from data protection regimes around sensitive personal data, to blockchain and smart contracts, platform data reuse, IP rights and limitations, and many other crucial concerns for law’s interventions. The book also engages with concerns about the ‘surveillance society’, for example regarding contact tracing technology used during the Covid-19 pandemic. The analytical approach provided will make this an excellent resource for scholars and educators, legal practitioners (from constitutional law to contract law) and policy makers within regulation and governance. The empirical case studies will also be of great interest to scholars of technology law and public policy. The regulatory community will find this collection offers an influential case for law’s relevance in giving institutional enforceability to ethics and principled design.Trade Review‘Regulatory Insights on Artificial Intelligence is bursting with ideas. While many more questions are asked than answered, those questions require one to think deeply about important issues associated with AI. That thinking is sorely needed now, if this technology is to benefit us, rather than harm us.’ -- Rob Clark, Intellectual Property Forum (IPSANZ)‘Regulatory Insights on Artificial Intelligence provides a timely and important discussion of the regulation of a technology that is not only proliferating into our lives, but becoming disruptive in our economic and social institutions. I highly recommend the book for legal scholars, regulators, and anyone interested in a comprehensive text on the topic.’ -- Woodrow Barfield, Visiting Professor, University of Turin, Italy‘This book is an excellent resource for aiding the discussion on the imminent need for effective regulation, informed by interdisciplinary and multi-stakeholder approaches, that AI governance requires. It is a must read for those interested in the “next steps” to actually implementing or codifying AI governance into existing legal contexts.’ -- Christoph Lütge, Technical University of Munich, GermanyTable of ContentsContents: Preface xi 1 Regulatory insights on artificial intelligence: research for policy 1 Mark Findlay and Jolyon Ford 2 Editors’ reflections 16 Mark Findlay and Jolyon Ford 3 Artificial intelligence and sensitive inferences: new challenges for data protection laws 19 Damian Clifford, Megan Richardson and Normann Witzleb 4 Revaluing labour? Secondary data imperialism in platform economies 46 Mark Findlay and Josephine Seah 5 Gauging the acceptance of contact-tracing technology: an empirical study of Singapore residents’ concerns and trust in information sharing 70 Ong Ee Ing and Loo Wee Ling 6 Regulating personal data usage in COVID-19 control conditions 101 Mark Findlay and Nydia Remolina 7 Editors’ reflections 128 Mark Findlay and Jolyon Ford 8 Coding legal norms: an exploratory essay 132 Will Bateman 9 Artificial intelligence and the unconscionability principle 150 Dilan Thampapillai 10 The possibilities of IF-THEN-WHEN 162 Sally Wheeler 11 Doing it online: is mediation ready for the AI age? 187 Nadja M Alexander 12 Editors’ reflections 214 Mark Findlay and Jolyon Ford 13 Ethical AI frameworks: the missing governance piece 218 Jolyon Ford 14 The accountability of algorithms on social media platforms 239 Philippa Ryan 15 Models and data trade regulation and the road to an agreement 261 Henry Gao Index

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Book SynopsisThis incisive Handbook offers novel theoretical and doctrinal insights alongside practical guidance on some of the most challenging issues in the field of artificial intelligence and intellectual property. Featuring all original contributions from a diverse group of international thought leaders, including top academics, judges, regulators and eminent practitioners, it offers timely perspectives and research on the relationship of AI to copyright, trademark, design, patent and trade secret law.The Handbook is divided into four thematic parts, beginning with topics that address the intersection of IP and AI broadly before focusing on issues associated with specific types of IP. Chapters tackle critical legal questions, from issues with protecting AI-generated ourput to the impact of AI on how trademarks are used, offering valuable lessons on technology regulation and how technological evolution will disrupt existing legal frameworks.Scholars and students of intellectual property law and its intersections with AI and related technologies will find this Handbook ’s cutting-edge contributions to be a crucial read. Its guidance on the practical legal implications of technological advances will also be of interest to IP practitioners, as well as policymakers and regulators.Trade Review‘A book of impressive breadth and thoughtfully curated analyses of doctrinal and policy issues at the intersection of AI and Intellectual Property (IP). The Handbook illuminates challenges across all IP fields and exposes the fragile normative bases on which many of our extant laws depend. It is a must have and a “go to” for meaningful engagement with the complex questions regarding the regulation of AI and IP — both nationally and globally.’ -- Ruth L. Okediji, Harvard Law School, USTable of ContentsContents: PART I MULTI-SUBJECT 1 Artificial intelligence and intellectual property: an introduction 2 Ryan Abbott 2 The human cause 21 Daniel J. Gervais 3 Considering intellectual property law for embodied forms of artificial intelligence 39 Woodrow Barfield, Argyro Karanasiou and Karni Chagnal-Feferkorn 4 AI replication of musical styles points the way to an exclusive rights regime 64 Sean M. O’Connor 5 The elusive intellectual property protection of trained machine learning models: a European perspective 83 Jean-Marc Deltorn 6 An abject failure of intelligence: intellectual property and artificial intelligence 112 Michael D. Pendleton PART II COPYRIGHT AND RELATED RIGHTS 7 The AI–copyright challenge: tech-neutrality, authorship, and the public interest 133 Carys J. Craig 8 Four theories in search of an A(I)uthor 155 Giancarlo Frosio 9 Copyright law should stay true to itself in the age of artificial intelligence 178 Alice Lee and Phoebe Woo 10 The protection of AI-generated pictures (photograph and painting) under copyright law 197 Yaniv Benhamou & Ana Andrijevic 11 Performers’ rights and artificial intelligence 217 Richard Arnold 12 AIn’t it just software? 224 Shubha Ghosh 13 Can artificial intelligence infringe copyright? Some reflections 244 Enrico Bonadio, Plamen Dinev and Luke McDonagh PART III TRADE MARKS AND DESIGNS 14 Computational trademark infringement and adjudication 258 Daryl Lim 15 Online shopping with artificial intelligence: what role for trade marks? 289 Anke Moerland and Christie Kafrouni 16 Trademark law, AI-driven behavioral advertising, and the Digital Services Act: toward source and parameter transparency for consumers, brand owners, and competitors 308 Martin Senftleben 17 A quotidian revolution: artificial intelligence and trade mark law 324 Dev S. Gangjee 18 The impact of AI on designs law 345 Trevor Cook PART IV PATENTS AND TRADE SECRETS 19 Legal fictions and the corporation as an inventive artificial intelligence 355 Dennis Crouch 20 Economic reasons to recognise AI inventors 375 Benjamin Mitra-Kahn 21 Reverse engineering (by) artificial intelligence 390 Shawn Bayern 22 Trade secrets versus the AI explainability principle 404 Rita Matulionyte and Tatiana Aranovich 23 The inventive step requirement and the rise of the AI machines 422 Noam Shemtov and Garry A. Gabison 24 Trade secrecy, factual secrecy and the hype surrounding AI 442 Sharon K. Sandeen and Tanya Aplin Index

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Book SynopsisDrawing on the theoretical debates, practical applications, and sectoral approaches in the field, this ground-breaking Handbook unpacks the political and regulatory developments in AI and big data governance. Covering the political implications of big data and AI on international relations, as well as emerging initiatives for legal regulation, it provides an accessible overview of ongoing data science discourses in politics, law and governance.With novel insights into existing and emerging debates, this cutting-edge Handbook highlights the mutual effects of big data and AI on society. Amongst other theoretical and sectoral issues, chapters analyse the liability of AI use in autonomous weapons, the role of big data in healthcare and education, the intersections between AI and gender in human rights law, and the ethics of public facial-recognition technology. Addressing the many open questions and future regulatory problems, it uses data science to investigate the dynamics between the technical aspects, societal dynamics and governance implications of big data and AI.Transdisciplinary in scope, this Handbook will be invaluable to students and researchers across the fields of politics, law, governance and data science, alongside policymakers concerned with the regulation and governance of AI and big data in public and private institutions.Trade Review‘Zwitter and Gstrein have astutely brought together an impressive collection of chapters that address key themes in the politics and governance of AI and big data. From social justice and gender to privacy and rights, the Handbook provides a solid introduction to key debates and their implications for societies.’ -- Evelyn Ruppert, Goldsmiths, University of London, UK‘This volume succeeds in bringing together a wide ranging collection of original studies in a field that is as fast developing as it is important to keep track of. The reader who is interested in normative political and governance perspectives on AI and big data will find insightful analyses and well-informed discussions of the key problems of regulation and policy making in a digital age.’ -- Jeroen van den Hoven, Delft University of Technology, the NetherlandsTable of ContentsContents: Foreword xiii PART I INTRODUCTION Introduction to the Handbook on the Politics and Governance of Big Data and Artificial Intelligence 2 Andrej Zwitter and Oskar J. Gstrein PART II CONCEPTUAL PERSPECTIVES 1 Can AI governance be progressive? Group interests, group privacy and abnormal justice 19 Linnet Taylor 2 Big Data and the humanitarian sector: emerging trends and persistent challenges 41 Susanne Schmuck, Andrej Zwitter and Oskar J. Gstrein 3 Digital twins: potentials, ethical issues and limitations 64 Dirk Helbing and Javier Argota Sánchez-Vaquerizo 4 Governing Digital Twin technology for smart and sustainable tourism: a case study in applying a documentation framework for architecture decisions 105 Eko Rahmadian, Daniel Feitosa and Andrej Zwitter PART III PRINCIPLE-BASED APPROACHES TO THE GOVERNANCE OF BIG DATA AND AI 5 Digital transitional justice: unpacking the black box 139 Christopher K. Lamont and Medlir Mema 6 Autonomous weaponry and IR theory: conflict and cooperation in the age of AI 167 Amelia Hadfield and Alex Leveringhaus 7 Understanding emergent technology, instability and power in international political economy 188 Malcolm Campbell-Verduyn 8 Governance of AI and gender: building on International Human Rights Law and relevant regional frameworks 211 Elizabeth Coombs and Halefom Abraha PART IV SECTORAL APPROACHES TO THE GOVERNANCE OF BIG DATA AND AI 9 Better technological security solutions through human-centred design and development 245 Andrew B. Wootton, Caroline L. Davey, Dagmar Heinrich and Maximilian Querbach 10 On the governance of privacy-preserving systems for the web: should Privacy Sandbox be governed? 279 Lukasz Olejnik 11 Experiments with facial recognition technologies in public spaces: in search of an EU governance framework 315 Catherine Jasserand 12 Big Data, AI and health data: between national, European, and international legal frameworks 358 Nikolaus Forgó, Emily Johnson, Iana Kazeeva and Elisabeth Steindl 13 Governing the ‘datafied’ school: bridging the divergence between universal education and student autonomy 395 Theresa Henne and Oskar J. Gstrein PART V AUTONOMOUS SYSTEMS, RIGHTS AND DUTIES 14 Artificial Intelligence and international human rights law: implications for humans and technology in the 21st century and beyond 430 Joshua C. Gellers and David J. Gunkel 15 Challenges posed by autonomous systems to liability regimes: finding a balance 456 Nynke E. Vellinga 16 Autonomous Weapons Systems in warfare: is Meaningful Human Control enough? 476 Taís Fernanda Blauth Index 504

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Book SynopsisArtificial Intelligence (AI) is being rapidly introduced into the workplace, creating debate around what AI means for our work and organizations. This book gives grounded counterweight to provocative newspaper headlines by using in-depth case studies of eight organizations’ experiences of implementing and using AI, providing readers with a solid understanding of what is actually happening in practice.Critical yet constructive, the authors address the challenges of implementing AI: organizing for data, testing and validating, algorithmic brokering, and changing work. Using a combination of existing literature and thorough practical examples, they provide answers to questions such as: What data do I need? When is a system good enough to actually take over tasks? And how can my employees be prepared for working with AI? The book presents four recommendations for WISE management of AI, requiring work-related insights, interdisciplinary knowledge, sociotechnical change processes, and ethical awareness.Offering insight into the unique characteristics of AI in organizations, this book will be essential reading for scholars of business and management, data analytics and information systems, technology and innovation, and computer science. With practical recommendations for managing the challenges of AI, it will also provide business managers with reflections to improve their own AI development and implementation processes.Trade Review‘Wonderfully written, this book will resonate with every manager who is currently grappling with implementing AI in their organization. By analyzing real-life case studies, the authors go way beyond the AI hype and dive into the intricate organizational and work challenges that arise with the introduction of AI in the workplace, providing actionable insights. A must read for all decision makers, developers and technology brokers at incumbent organizations!’ -- Stella Pachidi, University of Cambridge, UKTable of ContentsContents: 1. Introduction to managing AI wisely 2. What is AI? 3. Perspectives on AI and work 4. Methods and introduction to cases 5. Organizing for data 6. Testing and validating 7. Algorithmic brokers 8. Changing work 9. How can AI systems be managed wisely? Index

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Book SynopsisWith the advancement in processing power and storage now enabling algorithms to expand their capabilities beyond their initial narrow applications, technology is becoming increasingly powerful. This highly topical Handbook provides a comprehensive overview of the impact of Artificial Intelligence (AI) on work, assessing its effect on an array of economic sectors, the resulting nature of work, and the subsequent policy implications of these changes. Featuring contributions from leading experts across diverse fields, the Handbook of Artificial Intelligence at Work takes an interdisciplinary approach to understanding AI’s connections to existing economic, social, and political ecosystems. Considering a range of fields including agriculture, manufacturing, health care, education, law and government, the Handbook provides detailed sector-specific analyses of how AI is changing the nature of work, the challenges it presents and the opportunities it creates. Looking forward, it makes policy recommendations to address concerns, such as the potential displacement of some human labor by AI and growth in inequality affecting those lacking the necessary skills to interact with these technologies or without opportunities to do so.This vital Handbook is an essential read for students and academics in the fields of business and management, information technology, AI, and public policy. It will also be highly informative from a cross-disciplinary perspective for practitioners, as well as policy makers with an interest in the development of AI technology.Table of ContentsContents: 1 Introduction to the Handbook of Artificial Intelligence at Work: Interconnections and Policy Implications 1 Martha Garcia-Murillo and Ian MacInnes PART I CONCEPTUALIZING THE HUMAN WITH THE MACHINE 2 The computer says no: how automated decision systems affect workers’ role perceptions in socio-technical systems 16 Sabine T. Koeszegi, Setareh Zafari, and Reinhard Grabler 3 Responsible AI at work: incorporating human values 32 Andreas Theodorou and Andrea Aler Tubella 4 AI-enabled business model and human-in-the-loop (deceptive AI): implications for labor 47 Uma Rani and Rishabh Kumar Dhir 5 Tools for crowdworkers coding data for AI 76 Saiph Savage and Martha Garcia-Murillo PART II SECTORAL USES, APPLICATIONS, CHALLENGES, AND OPPORTUNITIES 6 AI and the transformation of agricultural work: economic, social, and environmental implications 96 Andrea Renda 7 AI in manufacturing and the role of humans: processes, robots, and systems 119 Panagiotis Stavropoulos, Kosmas Alexopoulos, Sotiris Makris, Alexios Papacharalampopoulos, Steven Dhondt, and George Chryssolouris 8 Workers and AI in the construction and operation of civil infrastructures 142 Jinding Xing, Zhe Sun, and Pingbo Tang 9 AI-based technology in home-based care in aging societies: challenges and opportunities 166 Naoko Muramatsu, Miloš Žefran, Emily Stiehl, and Thomas Cornwell 10 Artificial intelligence for professional learning 191 Wayne Holmes and Allison Littlejohn 11 Smart automation in entrepreneurial finance: the use of AI in private markets 212 Francesco Corea 12 The artificial creatives: the rise of combinatorial creativity from DALL-E to GPT-3 225 Giancarlo Frosio 13 The judicial system and the work of judges and lawyers in the application of law and sanctions assisted by AI 250 Karim Benyekhlef and Jie Zhu 14 AI and national security 276 Saiph Savage, Gabriela Avila, Norma Elva Chávez, and Martha Garcia-Murillo 15 Governance, government records, and the policymaking process aided by AI 291 Andrea Renda PART III THE LABOR IMPLICATIONS OF ARTIFICIAL INTELLIGENCE AT WORK 16 Recurrent memes and technological fallacies 315 David Heatly and Bronwyn Howell 17 AI and income inequality: the danger of exacerbating existing trends toward polarization in the US workforce 338 Dan Sholler and Ian MacInnes 18 The impact of AI on contracts and unionisation 356 Michael Walker Index 371

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Book SynopsisIs competition law able to deal with algorithmic collusion? This evaluative book provides an insight into tackling this important question for competition law, with contrasting critical perspectives, including theoretical, empirical, and doctrinal – the latter frequently from a comparative perspective.Bringing together scholarly discussion on algorithmic collusion, the book questions whether competition law is adeptly equipped to deal with its various facets. With a comprehensive overview of the recent literature on algorithmic collusion, chapters offer a critical appraisal of the effectiveness of competition law to deal with algorithmic collusion. Covering a unique collection of legal, theoretical, and experimental case studies, it initiates debate among legal scholars for a better understanding of the data upon which algorithms decide prices.With a comparative identification of both the potentialities and limitations of competition law in relation to algorithmic collusion, this book will be of key value to students and scholars of competition law, economics and finance. It will also be an invaluable resource for legal practitioners and policy makers in the field.Trade Review‘This book is essential reading for those with an interest in algorithmic collusion, or competition and technology more generally. I would also recommend it to those who have limited knowledge of this area of competition scholarship and feel overwhelmed by the sheer volume of existing literature available. This collection is an excellent starting point, as the early chapters are written in a very clear and accessible style. They do an excellent job of explaining the main issues and critically summarising and discussing the previous literature, setting the scene for the original contributions that follow.’ -- Andreas Stephan, Competition Policy Blog‘This unique book offers a window into the fascinating world of algorithmic collusion. Several contributions assess how this new phenomenon is dealt with under the laws of various jurisdictions (Australia, China, India, Japan and the EU). That alone would make the book worth the read, but there is more. Another chapter dives deep into the algorithms used by Uber and Amazon and draws implications on the likelihood of competition law infringements. Yet another chapter shares the results of a screening exercise for algorithmic collusion in Singapore. Together, the chapters in this book reflect the great diversity and originality of research into this topic, and take the reader on a wonderful journey through this novel area of competition law.’ -- Simon Vande Walle, The University of Tokyo, Japan‘In many online markets, prices are set in an automated manner by algorithms, which raises significant competitive concerns and numerous competition law issues, in particular the danger of collusive behaviour of algorithms. This collection of essays provides an excellent overview of the key economic and legal aspects of algorithmic collusion, as well as the approaches taken in different jurisdictions to address this problem. It is a valuable volume that should be consulted by all interested in algorithmic collusion and its economic and competition law aspects.’ -- Ulrich Schwalbe, University of Hohenheim, Germany.‘This collection of essays helps to improve our understanding across AI collusion.’ -- D. Daniel Sokol, USC Gould School of Law and Marshall School of Business, USTable of ContentsContents: Preface x Salil K. Mehra Acknowledgements xii 1 The algorithmic collusion debate: a focus on (autonomous) tacit collusion 1 Steven Van Uytsel 2 Algorithms and the limits of antitrust 39 Thomas Weck 3 Artificially intelligent collusion caught under EU competition law 48 Jan Blockx 4 Can the reformed Australian competition law stop algorithmic collusion? 67 Baskaran Balasingham 5 Tackling algorithmic collusion: the scope of the Indian Competition Act 92 Nikita Koradia, Kiran Manokaran and Zara Saeed 6 Challenges brought by and in response to algorithms: the perspective of China’s Anti-Monopoly Law 142 Wei Han, Yajie Gao and Ai Deng 7 Algorithmic collusion and the Japanese antimonopoly law 165 Steven Van Uytsel and Yoshiteru Uemura 8 Price-monitoring algorithms and resale price maintenance: an analysis of recent cases in Europe 189 Yoshiteru Uemura 9 Pricing in online grocery markets: challenges in monitoring competition 203 Cassey Lee and Gloria Lin 10 Algorithms unravelled: observations on the audit of Uber and Amazon marketplace algorithms 237 Steven Van Uytsel Index 260

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Book SynopsisThis provocative and timely book identifies and disrupts the conventional regulation and governance discourses concerning AI and big data. It suggests that, instead of being used as tools for exclusionist commercial markets, AI and big data can be employed in governing digital transformation for social good. Analysing the ways in which global technology companies have colonised data access, the book reveals how trust, ethics, and digital self-determination can be reconsidered and engaged to promote the interests of marginalised stakeholders in data arrangement. Chapters examine the regulation of labour engagement in digital economies, the landscape of AI ethics, and a multitude of questions regarding participation, costs, and sustainability. Presenting several informative case studies, the book challenges some of the accepted qualifiers of frontier tech and data use and proposes innovative ways of actioning the more conventional regulatory components of big data. Scholars and students in information and media law, regulation and governance, and law and politics will find this book to be critical reading. It will also be of interest to policymakers and the AI and data science community.Trade Review‘Based on wisely selected case studies, the authors offer a compelling reframing of the orthodox tech-and-regulation relationship. They build a strong case that AI is more than a regulatory target: “Distruptive Regulation” uses technology to protect and advance the interests of vulnerable stakeholders instead of serving those in power.’ -- Urs Gasser, Technical University of Munich, Germany‘If you're looking for a thought-provoking read on governing AI and big data, then I highly recommend checking out this book. Using real-life examples, the authors offer a new approach to regulation that empowers people and promotes trust and data responsibility. The authors also provide practical pathways to advance digital self-determination and to promote fairness, and non-discrimination in how we use AI. Overall, the book challenges conventional thinking and is a must-read for anyone interested in technology and its impact on our society.’ -- Stefaan G. Verhulst, New York University, USTable of ContentsContents: 1. Disruptive regulation 2. Trust as regulation 3. Disrupting data – digital self-determination 4. Modern AI ethics is a field in the making 5. Modelling disruptive regulation Index

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Book SynopsisThis forward-looking Research Handbook makes an insightful contribution to the emerging field of studies on communication of, by and with AI. Bringing together state-of-the-art research from over 50 leading international scholars across various fields, it provides a comprehensive overview of the complex intersections between AI and communication. The team of expert contributors explore key conceptual, theoretical and methodological approaches and examine a variety of ethical considerations, legal issues and policy implications of AI across diverse contexts. The Handbook spans a wide range of topics related to AI-empowered, immersed, mediated and integrated communications. These range from the role of news media and digital communication platforms in constructing, representing and framing AI across different countries and cultures, to the public understanding of, attitude towards and interaction with AI and its related technologies. Offering foundational guidance on AI and communication, the Research Handbook will stimulate further intellectual inquiry for future scholarship in this rapidly evolving area. Cross-disciplinary in scope, this dynamic Research Handbook will prove an essential reference for students and scholars in multiple fields, including communication, computer science, data and information science, sociology, business, and education. Policymakers and practitioners will also find it a valuable resource to help inform AI-related regulations and policies.Trade Review‘This is an essential and refreshing collection of work that examines some of the most crucial questions facing our communication and media systems. It is sure to help guide research over the next decade.’ -- Siva Vaidhyanathan, University of Virginia, USTable of ContentsContents: Preface xvi Introduction to the Research Handbook on Artificial Intelligence and Communication xvii Seungahn Nah PART I MAPPING RESEARCH ON ARTIFICIAL INTELLIGENCE AND COMMUNICATION 1 A systematic review of scholarship in AI and communication research (1990–2022) 2 Sumita Louis and Seungahn Nah 2 AI-integrated communication: conceptualization and a critical review 29 Donghee Yvette Wohn and Mashael Almoqbel 3 Toward a sociology of machines: an interviewing methodology for human–machine communication 44 Cait Lackey 4 Discovering developmental trajectories and trends of conversational agent research using dynamic topic modeling 58 Hüseyin Özçinar and Aylin Sabanci Bayramoğlu 5 A systematic review of scholarship on metaverse 79 Jun Luo, Sumita Louis, and Seungahn Nah PART II FRAMING ARTIFICIAL INTELLIGENCE 6 AI in schools and universities: mapping central debates through enthusiasms and concerns 94 Kristjan Kikerpill and Andra Siibak 7 How news organizations and journalists understand artificial intelligence: application of news language database to AI-related news stories 108 Jeongsub Lim 8 AI in Portugal: news framing, tone, and sources 125 Paulo Nuno Vicente 9 AI bias, news framing, and mixed-methods approach 145 Jun Luo, Seungahn Nah, and Jungseock Joo PART III PUBLIC UNDERSTANDING OF ARTIFICIAL INTELLIGNECE 10 Risk perceptions and trust mechanisms related to everyday AI 163 Hichang Cho and Rosalie Hooi 11 Fearing the future: examining the conditional indirect correlation of attention to artificial intelligence news on artificial intelligence attitudes 176 Alex Kirkpatrick, Jay D. Hmielowski, and Amanda Boyd 12 A machine-learning approach to assessing public trust in AI-powered technologies 193 Poong Oh and Younbo Jung 13 Machine learning and deep learning for social science: a bibliometric approach 214 Jang Hyun Kim and Dongyan Nan 14 AI and data-driven political communication (re)shaping citizen–government interactions 231 Jérôme Duberry 15 AI folk tales: how nontechnical publics make sense of artificial intelligence 246 Barbara Pohl and Lauri Goldkind PART IV INTERACTING WITH ARTIFICIAL INTELLIGENCE 16 Facilitating stakeholder communication around AI-enabled systems and business processes 268 Matthew Bundas, Chasity Nadeau, Thanh H. Nguyen, Jeannine Shantz, Marcello Balduccini, Edward Griffor, and Tran Cao Son 17 The levels of automation and autonomy in the AI-augmented newsroom: toward a multi-level typology of computational journalism 284 Hannes Cools, Baldwin Van Gorp, and Michaël Opgenhaffen 18 AI as communicative other: critical relationality in human–AI communication 300 Marco Dehnert 19 Needs and practices for AI-mediated messaging in uncertain circumstances 315 Adam M. Rainear, Patric R. Spence, and Kenneth A. Lachlan 20 Why wasn’t I ready for that? Suggestions and research directions for the use of machine agents in organizational life 325 Patric R. Spence 21 The Media Are Social Actors paradigm and beyond: theory, evidence, and future research 337 Kun Xu, Fanjue Liu, Xiaobei Chen, and Matthew Lombard PART V POLICING ARTIFICIAL INTELLIGENCE 22 Evaluating the self-disclosure of personal information to AI-enabled technology 355 Jessica K. Barfield 23 To reimagine more deeply: understanding what AI communicates 376 John S. Seberger, Hyesun Choung, and Prabu David 24 Automated inequalities: examining the social implications of artificial intelligence in China 391 Bibo Lin and Joanne Kuai 25 Design + power: policy for the ecology of influence 405 Jasmine McNealy Index 418

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Book SynopsisThis engaging Handbook identifies and critically examines the moral opportunities and challenges typically attributed to artificial intelligence. It provides a comprehensive overview and examination of the most pressing and urgent problems with this technology by drawing on a wide range of analytical methods, traditions, and approaches.

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Book SynopsisThis timely Companion provides a comprehensive overview of the relationship between applied ethics and the development and use of Artificial Intelligence (AI). Adopting a holistic approach, an array of global experts identify the norms at stake, map the legal landscape, and contextualize normative expectations in relevant use cases of AI.

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Book SynopsisAs artificial intelligence (AI) continues to seep into more areas of society and culture, critical social perspectives on its technologies are more urgent than ever before. Bringing together state-of-the-art research from experienced scholars across disciplines, this Handbook provides a comprehensive overview of the current state of critical AI studies. Moving beyond narrow technological definitions of AI, the Handbook provides readers with an in-depth understanding of its social, ethical and political implications. Chapters cover a broad range of timely issues related to AI, including the risk of bias and discrimination in its systems, its impact on democracy and governance, concerns surrounding privacy and surveillance, and the use of its technologies in decision-making processes. Underscoring the urgent need for deeper critical analyses of AI, the Handbook constitutes a major contribution to the ongoing discussion about what critical studies of AI can entail, what questions they may pose, and what concepts they can offer to address them. Rich in theoretical and empirical analysis, this cutting-edge Handbook will prove an invaluable resource for students and scholars of digital sociology and science and technology studies. Its extensive coverage of this emerging field will also appeal to practitioners, developers and policymakers seeking orientation in the complex social and political dynamics of AI.Trade Review‘AI is not only technology; it also means power. In times when AI ethics is often closely aligned with big tech and when AI teams are expelled or undervalued, a critical view of AI is much needed. Addressing a diversity of aspects from political economy to sociotechnological imaginaries and activism, this Handbook offers a range of critical scholarship on AI that shows how AI is entangled with the social structures and power relations in society. A welcome antidote to the ideologies of technological optimism, technodeterminism, and technosolutionism, and great support for the critical and interdisciplinary project of developing technology that contributes to, rather than undermines, conviviality and the common good.’ -- Mark Coeckelbergh, University of Vienna, Austria‘AI has proliferated in everyday life. Virtual assistants such as Alexa and Siri are present on our phones and in our homes. More and more people use robotic lawnmowers and robot hoovers. There are bots on the Internet that post, comment, and like. Robots and AI have changed the world of work. ChatGPT has given us an impression of how online search could look like in the future. The world’s largest military forces are investing heavily into the development of AI. We need to better understand what impacts AI has on society. For doing so, we need critical theories and analysis of AI. The Handbook of Critical Studies of Artificial Intelligence provides 75 chapters that help us to better understand what it means to critically study AI in society. This book is excellent reading for everyone interested in AI & society.’ -- Christian Fuchs, Paderborn University, GermanyTable of ContentsContents: 1 Introducing critical studies of artificial intelligence 1 Simon Lindgren PART I AI AND CRITICAL THEORY: CONCEPTUAL DISCUSSIONS 2 Recursive power: AI governmentality and technofutures 21 Fenwick McKelvey and Jonathan Roberge 3 The danger of smart ideologies: counter-hegemonic intelligence and antagonistic machines 33 Peter Bloom 4 The becoming of AI: a critical perspective on the contingent formation of AI 43 Anna Jobin and Christian Katzenbach 5 Artificial intelligence and the problem of radical uncertainty 56 Robert Holton 6 Trading human autonomy for technological automation 67 Simona Chiodo 7 Automation anxiety: a critical history – the apparently odd recurrence of debates about computation, AI and labour 79 Caroline Bassett and Ben Roberts 8 AI, critical knowledge and subjectivity 94 Eran Fisher 9 Habits and habitus in algorithmic culture 108 Stefka Hristova 10 Algorithms and emerging forms of intimacy 117 Tanja Wiehn 11 It’s incomprehensible: on machine learning and decoloniality 128 Abeba Birhane and Zeerak Talat 12 Pragmatism and AI: a critical approach 141 Johnathan Flowers 13 Digital humanism and AI 152 Wolfgang Hofkirchner and Hans-Jörg Kreowski 14 Beyond AI solutionism: toward a multi-disciplinary approach to artificial intelligence in society 163 Simon Lindgren and Virginia Dignum 15 Artificial intelligence and social memory: towards the cyborgian remembrance of an advancing mnemo-technic 173 Samuel Merrill 16 Making sense of AI-influenced geopolitics using STS theories 187 Arun Teja Polcumpally PART II AI IMAGINARIES AND DISCOURSES 17 Bothering the binaries: unruly AI futures of hauntings and hope at the limit 199 Amanda Lagerkvist and Bo Reimer 18 Imaginaries of artificial intelligence 209 Vanessa Richter, Christian Katzenbach and Mike Schäfer 19 Language of algorithms: agency, metaphors and deliberations in AI discourses 224 Kaisla Kajava and Nitin Sawhney 20 Technological failures, controversies and the myth of AI 237 Andrea Ballatore and Simone Natale 21 Marking the lines of artificial intelligence 245 Mario Verdicchio 22 The critical potential of science fiction 254 Miroslav Kotásek 23 A critical review of news framing of artificial intelligence 266 Ching-Hua Chuan 24 Media representations of artificial intelligence: surveying the field 277 Saba Rebecca Brause, Jing Zeng, Mike S. Schäfer and Christian Katzenbach 25 Educational imaginaries of AI 289 Lina Rahm PART III THE POLITICAL ECONOMY OF AI: DATAFICATION AND SURVEILLANCE 26 Critical AI studies meets critical political economy 302 Pieter Verdegem 27 The industry of automating automation: the political economy of the AI industry 312 James Steinhoff 28 AI, class societies and the social life of reason 323 Scott Timcke 29 Re-imagining democracy: AI’s challenge to political theory 333 Guy Paltieli 30 AI as automated inequality: statistics, surveillance and discrimination 343 Mike Zajko 31 Digital tracking and infrastructural power 354 Stine Lomborg, Rasmus Helles and Signe Sophus Lai 32 AI and the everyday political economy of global health 367 Michael Strange and Jason Tucker 33 Addressing global inequity in AI development 378 Chinasa T. Okolo PART IV AI TRANSPARENCY, ETHICS AND REGULATION 34 A critical approach to AI ethics 391 Rosalie A. Waelen 35 Power and inequalities: lifting the veil of ignorance in AI ethics 402 Anais Resseguier 36 Barriers to regulating AI: critical observations from a fractured field 413 Ashlin Lee, Will Orr, Walter G. Johnson, Jenna Imad Harb and Kathryn Henne 37 Why artificial intelligence is not transparent: a critical analysis of its three opacity layers 424 Manuel Carabantes 38 How to critique the GDPR: when data protection is turned against the working class 435 Carl Öhman 39 Four facets of AI transparency 445 Stefan Larsson, Kashyap Haresamudram, Charlotte Högberg, Yucong Lao, Axel Nyström, Kasia Söderlund and Fredrik Heintz 40 An inclusive approach to ascribing responsibility in robot ethics 456 Janina Loh 41 Machines and morals: moral reasoning ability might indicate how close AI is to attaining true equivalence with human intelligence 470 Sukanto Bhattacharya 42 A women’s rights perspective on safe artificial intelligence inside the United Nations 481 Eleonore Fournier-Tombs 43 From ethics to politics: changing approaches to AI education 493 Randy Connolly 44 The transparency of reason: ethical issues of AI art 504 Dejan Grba PART V AI BIAS, NORMATIVITY AND DISCRIMINATION 45 Learning about human behavior? The transcendental status of grammars of action in the processing of HCI data 516 Andreas Beinsteiner 46 Algorithmic moderation: contexts, perceptions, and misconceptions 528 João Gonçalves and Ina Weber 47 Algorithmic exclusion 538 Kendra Albert and Maggie Delano 48 Prospective but disconnected partners: AI-informed criminal risk prediction 549 Kelly Hannah-Moffat and Fernando Avila 49 Power asymmetries, epistemic imbalances and barriers to knowledge: the (im)possibility of knowing algorithms 563 Ana Pop Stefanija 50 Gender, race and the invisible labor of artificial intelligence 573 Laila Brown 51 Machine learning normativity as performativity 584 Tyler Reigeluth 52 Queer eye on AI: binary systems versus fluid identities 595 Karin Danielsson, Andrea Aler Tubella, Evelina Liliequist and Coppélie Cocq 53 Representational silence and racial biases in commercial image recognition services in the context of religion 607 Anton Berg and Katja Valaskivi 54 Social media as classification systems: procedural normative choices in user profiling 619 Severin Engelmann and Orestis Papakyriakopoulos 55 From hate speech recognition to happiness indexing: critical issues in datafication of emotion in text mining 631 Salla-Maaria Laaksonen, Juho Pääkkönen and Emily Öhman PART VI POLITICS AND ACTIVISM IN AI 56 Democratic friction in speech governance by AI 643 Niva Elkin-Koren and Maayan Perel 57 Automating empathy: overview, technologies, criticism 656 Andrew McStay and Vian Bakir 58 Ideational tensions in the Swedish automation debate: initial findings 670 Kalle Eriksson 59 En-countering AI as algorhythmic practice 682 Shintaro Miyazaki 60 Introducing political ecology of Creative-Ai 691 Andre Holzapfel PART VII AI AND AUTOMATION IN SOCIETY 61 Automated decision-making in the public sector 705 Vanja Carlsson, Malin Rönnblom and Andreas Öjehag-Pettersson 62 The landscape of social bot research: a critical appraisal 716 Harry Yaojun Yan and Kai-Cheng Yang 63 Introducing robots and AI in human service organizations: what are the implications for employees and service users? 726 Susanne Tafvelin, Jan Hjelte, Robyn Schimmer, Maria Forsgren, Vicenc Torra and Andreas Stenling 64 Critically analyzing autonomous materialities 737 Mikael Wiberg 65 Exploring critical dichotomies of AI and the Rule of Law 749 Markus Naarttijärvi 66 The use of AI in domestic security practices 763 Jens Hälterlein 67 Methodological reflections on researching the sociotechnical imaginaries of AI in policing 773 Carrie B. Sanders and Janet Chan 68 Emergence of artificial intelligence in health care: a critical review 783 Annika M. Svensson and Fabrice Jotterand 69 The politics of imaginary technologies: innovation ecosystems as political choreographies for promoting care robotics in health care 793 Jaana Parviainen 70 AI in education: landscape, vision and critical ethical challenges in the 21st century 804 Daniel S. Schiff and Rinat Rosenberg-Kima 71 Critically assessing AI/ML for cultural heritage: potentials and challenges 815 Anna Foka, Lina Eklund, Anders Sundnes Løvlie and Gabriele Griffin 72 AI ethnography 826 Anne Dippel and Andreas Sudmann 73 Automating social theory 845 Ralph Schroeder 74 Artificial intelligence and scientific problem choice at the nexus of industry and academia 859 Steve G. Hoffman 75 Myths, techno solutionism and artificial intelligence: reclaiming AI materiality and its massive environmental costs 869 Benedetta Brevini 76 AI governance and civil society: the need for critical engagement 878 Megan LePere-Schloop and Sandy Zook Index 891

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Book SynopsisWith AI advancements eliciting imminent changes to our transport systems, this enlightening Handbook presents essential research on this evolution of the transportation sector. It focuses on not only urban planning, but relevant themes in law and ethics to form a unified resource on the practicality of AI use.The Handbook on Artificial Intelligence and Transport provides a full investigation of the most recent AI transport developments, authored by an international collective of renowned contributors. Chapters examine several often challenging topics such as autonomous driving and cyber security ethics. They conclude that AI technology is likely to offer resolutions to persistent transport issues that have been almost impossible to solve using conventional approaches.This timely Handbook will be an important resource for students of transport planning and engineering, innovation and regional law. It will also benefit practitioners within the sectors of urban planning and engineering seeking updated evidence on the role of AI in transport improvement.Trade Review‘Under the astute editorship of Hussein Dia, the Handbook on Artificial Intelligence and Transport deftly elucidates a panoply of AI advancements across a myriad of transportation spheres. An indispensable tome for both academia and industry, it propels the transportation field towards a future replete with innovation and sagacity.’ -- Der-Horng Lee, Zhejiang University-University of Illinois Urbana-Champaign InstituteTable of ContentsContents: Introduction to the Handbook on Artificial Intelligence and Transport 1 Hussein Dia PART I SHORT-TERM TRAFFIC FORECASTING AND CONGESTION PREDICTION 1 A comparative evaluation of established and contemporary deep learning traffic prediction methods 14 Ta Jiun Ting, Scott Sanner, and Baher Abdulhai 2 Fault tolerance and transferability of short-term traffic forecasting hybrid AI models 47 Rusul Abduljabbar, Hussein Dia, and Pei-Wei Tsai 3 A review of deep learning-based approaches and use cases for traffic prediction 80 Rezaur Rahman, Jiechao Zhang, and Samiul Hasan 4 The ensemble learning process for short-term prediction of traffic state on rural roads 102 Arash Rasaizadi, Fateme Hafizi, and Seyedehsan Seyedabrishami 5 Using machine learning and deep learning for traffic congestion prediction: a review 124 Adriana-Simona Mihaita, Zhulin Li, Harshpreet Singh, Nabin Sharma, Mao Tuo, and Yuming Ou PART II PUBLIC TRANSPORT PLANNING AND OPERATIONS 6 The potential of explainable deep learning for public transport planning 155 Wenzhe Sun, Jan-Dirk Schmöcker, Youxi Lai, and Koji Fukuda 7 Neural network approaches for forecasting short-term on-road public transport passenger demands 176 Sohani Liyanage, Hussein Dia, Rusul Abduljabbar, and Pei-Wei Tsai PART III RAILWAYS 8 Artificial intelligence in railway traffic planning and management Taxonomy, a systematic review of the state-of-the-art of AI, and transferability analysis 222 Ruifan Tang, Zhiyuan Lin, Ronghui Liu, Rob M.P. Goverde, and Nikola Bešinović 9 Artificial intelligence in railways: current applications, challenges, and ongoing research 249 Lorenzo De Donato, Ruifan Tang, Nikola Bes̆inović, Francesco Flammini, Rob M.P. Goverde, Zhiyuan Lin, Ronghui Liu, Stefano Marrone, Elena Napoletano, Roberto Nardone, Stefania Santini, Valeria Vittorini PART IV FREIGHT AND AVIATION 10 Artificial intelligence and machine learning applications in freight transport 285 Yijie Su, Hadi Ghaderi, and Hussein Dia 11 A paradigm shift in the aviation industry with digital twin, blockchain, and AI technologies 323 Tommy Cheung, Bo Li, and Zheng Lei PART V VIDEO ANALYTICS AND MACHINE VISION APPLICATIONS 12 A deep learning approach to real-time video analytics for people and passenger counting 348 Chris McCarthy, Hadi Ghaderi, Prem Prakash Jayaraman, and Hussein Dia 13 AI machine vision for safety and mobility: an autonomous vehicle perspective 380 Sagar Dasgupta, Xishi Zhu, Muhammad Sami Irfan, Mizanur Rahman, Jiaqi Gong, and Steven Jones PART VI DATA ANALYTICS AND PATTERN ANALYSIS 14 A review of AI-enabled and model-based methodologies for travel demand estimation in urban transport networks 411 Sajjad Shafiei and Hussein Dia 15 Recombination-based two-stage out-of-distribution detection method for traffic flow pattern analysis 434 Yuchen Lu, Ying Jin, and Xi Chen 16 An intelligent machine learning alerting system for distracted pedestrians 465 M.L. Cummings, Lixiao Huang, and Michael Clamann PART VII PREDICTIVE TRAFFIC SIGNAL CONTROL 17 A critical review of traffic signal control and a novel unified view of reinforcement learning and model predictive control approaches for adaptive traffic signal control 482 Xiaoyu Wang, Baher Abdulhai, and Scott Sanner PART VIII AI ETHICS AND CYBERSECURITY CHALLENGES 18 A review of AI ethical and moral considerations in road transport and vehicle automation 534 Dorsa Alipour and Hussein Dia 19 Cybersecurity challenges in AI-enabled smart transportation systems 567 Lyuyi Zhu, Ao Qu, and Wei Ma 20 Autonomous driving: present and emerging trends of technology, ethics, and law 596 Gustav Lindberg, Ikeya Carrero, Fermín Mallor, Julián Estévez, Manuela Battaglini, and Ricardo Vinuesa Index 617

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Book SynopsisDevelopment in any country is impossible if reliable and affordable energy, safe water and sanitation, as well as telecommunication facilities, are not easily accessible. Artificial intelligence and machine learning techniques are now widely used in all branches of engineering to build and optimize systems. The combination of AI and engineering can indeed act as a real catalyst to achieve the UN SDGs. The volume editors present an analysis of different concepts and case studies in engineering disciplines such as chemical, civil, electrical, telecommunications and mechanical engineering, demonstrating how engineering systems and processes can leverage the power of AI to drive and achieve the UN SDGs. Topics covered include sustainable crop production and consumption, AI based clean water and sanitation monitoring, intelligent transport systems and achieving affordable and clean energy through AI and 5G powered internet of energy. Such a study is of paramount importance and is a valuable source of information for researchers, engineers, and policy makers to be able to better design and adopt AI enabled techniques in different engineering areas, with a view to catalyze the achievement of the UN SDGs.Table of ContentsChapter 1. Advances of Artificial Intelligence in Engineering; Tulsi Pawan Fowdur, Satyadev Rosunee, Robert T. F. Ah King, Pratima Jeetah and Mahendra Gooroochurn Part 1: Impact of AI Enabled Chemical and Environmental Engineering Systems on UN SDGs Chapter 2. Adoption of machine learning for sustainable solid waste management; P D Jeetah, G D Somaroo, D Surroop, A K Ragen, and N S Amode Chapter 3. Smart fertilizer application in agricultural land for sustainable crop production and consumption; Robert T. F. Ah King, Bhimsen Rajkumarsingh, Pratima Jeetah, Geeta Somaroo, and Deejaysing Jogee Chapter 4. Predicting household plastic level consumption using machine learning and AI; Jeetah P D , Chuttur, Hurry N , Tahalooa K , and Seebun D Chapter 5. Ant colony, bee colony and elephant herd optimizations for estimating aqueous-phase adsorption model parameters; Ackmez Mudhoo, Gaurav Sharma, Khim Hoong Chu, and Mika Sillanpää Part 2: Impact of AI Enabled Civil Engineering Systems on UN SDGs Chapter 6. Artificial Intelligence based clean water and sanitation monitoring; Deejaysing Jogee, Manta Devi Nowbuth, Virendra Proag, and Jean-Luc Probst Chapter 7. Achieving SDG targets in the land transport sector using Intelligent Transportation Systems; Zaheer Doomah, Asish Seeboo, and Tulsi Pawan Fowdur Part 3: Impact of AI Enabled Electrical Electronic and Telecommunications Engineering Systems on UN SDGs Chapter 8. Achieving affordable and clean Energy through AI and 5G Powered Internet of Energy (IoE); Tulsi Pawan Fowdur and Ashven Sanghan Chapter 9. Leveraging the power of Blockchain in Industry 4.0 and Intelligent Real-time Systems for achieving the SDGs; Tulsi Pawan Fowdur, Visham Hurbungs, and, and Lavesh Babooram Chapter 10. A Reliability-based Two Stage PMU Placement Optimisation Model using Mathematical and Nature-based Evolutionary Algorithms; Robert T. F. Ah King and Samiah Mohangee Chapter 11. Quantitative Assessment of Models and Indices for Interior Thermal Comfort taking into account the Effects of Solar Radiation and Wind; Bhimsen Rajkumarsingh, Robert T. F. Ah King, and Khalid Adam Joomun Chapter 12. The role of the Internet of Things for a more Sustainable Future; Anshu Prakash Murdan, and Vishwamitra Oree Part 4: Impact of AI Enabled Mechanical and Mechatronics Engineering Systems on UN SDGs Chapter 13. Mechatronics implementation of passive building elements to improve thermal comfort and promote energy efficiency in buildings; Mahendra Gooroochurn Chapter 14. Demystifying climate change and climate action through the circular homes concept - an educational tool for community engagement; Mahendra Gooroochurn Chapter 15. Robotics and automated systems for enabling an Industry 4.0 transformation in Mauritius; Mahendra Gooroochurn and Riaan Stopforth Chapter 16. Potential beneficial impact of AI-driven atmospheric corrosion prediction on the UN SDGs; Yashwantraj Seechurn Chapter 17. In-situ Durability Assessment of Natural Composite Structures by Considering ANN Modelling; Ramful Raviduth Part 5: Impact of AI Enabled Sustainability and Enterprise Development on UN SDGs Chapter 18. The Manufacturing Sector in Mauritius: Building Supply Chain Resilience & Business Value with AI; Satyadev Rosunee and Roshan Unmar Chapter 19. AI for Social Good: Opportunities for Inclusive & Sustainable Development; Satyadev Rosunee and Roshan Unmar Chapter 20. The Applications of Artificial Intelligence in the Textile Industry; Naraindra Kistamah

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Book SynopsisArtificial Intelligence for Sustainable Value Creation provides a detailed and insightful exploration of both the possibilities and the challenges that accompany widespread Artificial Intelligence.Providing a cutting edge analysis of the impact of AI in business and society, the editors offer an opportunity to assess what is known about managing other forms of information systems, strategy, and marketing, and to re-examine this knowledge in situations involving AI. This comprehensive book explores how human- centric AI systems create value inside organizations, distinguishing three main components: ethical value, societal value, and business value.Using a multidisciplinary perspective, this discerning book addresses the interests of a wide spectrum of practitioners, students, and researchers alike who are interested in identifying the value generated by AI systems in management.Trade Review‘This book is current and to the point. It makes a cutting-edge contribution to the field and is an extremely valuable asset for practitioners, students, and researchers alike who are interested in identifying value generated by AI systems in management and discovering opportunities and challenges.’ -- Leon Wang, International Journal of Data Science‘The analyses put forward, while being characterized by scholarly rigor, are accessible to anyone interested in understanding how AI creates value for management practitioners, be they the practitioners themselves, students, or scholars. The editors boldly choose to promote a holistic view of value creation which does not solely focus on economic performance. The Covid-19 pandemic has made companies more digitally agile, and practitioners must be informed about AI to remain competitive now more than ever. Furthermore, the book does not bow to the general hype around AI but aims at providing a nuanced view of what AI systems are today, based on rigorous empirical investigation rather than speculation. The book is also the perfect springboard for scholars and Phd students who wish to conduct further research related to AI and management, which is a hot topic in academia as well.’ -- Nathan Sorin, Micro & Macro Marketing‘What Pagani and Champion have achieved with Artificial Intelligence for Sustainable Value Creation is a seminal pragmatic roadmap prioritizing people and the planet via systems level design. AI technologies empower the metrics of success that humans provide and this book shows why values-driven sustainability is the only path forward for businesses to achieve a purpose driven future for their organizations and the world.’ -- John C. Havens, author of Heartificial Intelligence: Embracing Our Humanity to Maximize Machines‘The editors and the authors are to be congratulated for this important book. They show that AI can play a key, ethical role in the creation of societal, environmental, and business value by helping businesses gain better information, optimise operations, improve products and services, and become more competitive and sustainable.’ -- Luciano Floridi, University of Oxford, UK‘Artificial Intelligence by itself is just the latest in a long series of techno buzzwords. However, when you pair AI with the essential idea of Value Creation, as does this book, now you have something of critical importance to anyone who wants to know about the future of business!’ -- Charles Hofacker, Florida State University, USTable of ContentsContents: Foreword by Luc Julia xiv Acknowledgements xvi Introduction to Artificial Intelligence for Sustainable Value Creation 1 Margherita Pagani and Renaud Champion PART I HUMAN-CENTRIC AI 1 Creating business value through human-centric AI 9 Margherita Pagani and Renaud Champion 2 Value-driven design of AI enabled experiences 32 Yihyun Lim PART II BUSINESS VALUE 3 Digital platform ecosystems: the coming context for AI 55 Omar El Sawy, Milan Miric and Margherita Pagani 4 Unlocking value from AI in financial services: strategic and organizational tradeoffs vs. media narratives 70 Gianvito Lanzolla, Simone Santoni and Christopher Tucci PART III ETHICAL AND SOCIETAL VALUE 5 The challenge of responsible AI 99 Christine Balagué 6 A model of fair and explainable artificial intelligence 122 Amy Wenxuan Ding 7 Ethical maintenance of artificial intelligence systems 151 Boris Düdder, Florian Möslein, Norman Stürtz, Magnus Westerlund and Roberto V. Zicari Afterword 172 Margherita Pagani and Renaud Champion Index

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Book SynopsisAutonomous systems are on the frontiers of Artificial Intelligence (AI) research, and they are slowly finding their business applications. Driven mostly by Reinforcement Learning (RL) methods (one of the most difficult, but also the most promising modern AI algorithms), autonomous systems help create self-learning and self-optimising systems, ranging from simple game-playing agents to robots able to efficiently act in completely new environments. Based on in-depth study of more than 100 projects, Andrzej Wodecki explores RL as a key component of modern digital technologies, its real-life applications to activities in a value chain and the ways in which it impacts different industries. Artificial Intelligence in Management will help project leaders, decision makers and investors evaluate new autonomous projects and will serve as an inspiring guide for future research.Trade Review‘This publication is a very useful guide for organization administrators who hope to optimize operation management with proper applications of AI. For readers who have already known AI and management science, and is trying to integrate AI into management for new strategies and modes, or who want to extend their knowledge of AI in management, this book must be an ideally enlightening resource and will serve as an inspiring guide for future studies.’ -- Ren Yuan, International Journal of Knowledge-Based OrganizationsTable of ContentsContents: Introduction: From Intelligent Machines To Self-Driven Organizations 1. Artificial Intelligence And Machine Learning Landscape 2. The Impact Of Autonomous Systems On Technologies, Processes And Industry Sectors 3. Autonomous Systems In Value Generation 4. Prospects For The Future Conclusions Index

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Book SynopsisThis cutting-edge Handbook offers a comprehensive introduction to the emerging research field of artificial intelligence (AI) in human resource management (HRM). Broadly mapping AI fields relevant for HR, it not only considers the more well-known areas of machine learning and natural language processing, but also lesser-known fields such as affective computing and robotic process automation.Expert contributors analyze the applications of machine learning in human resources, including machine learning on text data, audio and video data, social media data, and in recruiting and staffing. They also explore a range of innovative topics such as knowledge representation and reasoning, and evolutionary computing. Discussing the explainability, fairness, accountability, and legitimacy of AI in HR, chapters bring normative issues to the fore. Approaches to researching AI in HR and to employing AI in HR research are also tackled. Offering an insight into existing research on artificial intelligence in human resources, the Handbook introduces core issues and considers implications for future research.This Handbook will be critical reading for scholars and students of human resource management, knowledge management, organizational innovation, computer science, and information systems. It will also be beneficial for practitioners in these fields.Trade Review‘This Handbook is a must-have whether you know a little or a lot about AI and human resource management. Topics range from the highly technical for specialists to the more foundational for novices. Readers can dive in to get answers to specific questions or read the whole volume to gain a thorough grounding. AI is here to stay in human resource management. It poses many challenges for scholars and practitioners. This Handbook is a great guide for addressing those challenges.' -- Mark Lengnick-Hall, University of Texas at San Antonio, US‘The potential for better decisions in managing people and also the conflicts between AI principles and those that have governed human resources are profound. This Handbook offers the most detailed and wide-ranging account available as to what AI solutions look like in this realm, not just those available now but most importantly those in the works.’ -- Peter Cappelli, University of Pennsylvania, US‘This Handbook provides a comprehensive overview of how AI might be deployed in the field of human resources and includes incisive analysis of some of the key challenges: explaining AIs’ decisions, fairness and legal regulation. It is a really excellent resource.’ -- Andy Charlwood, University of Leeds, UKTable of ContentsContents: Preface xii 1 Artificial intelligence in human resources – an introduction 1 Stefan Strohmeier PART I APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCES PART I.1 APPLICATIONS OF MACHINE LEARNING IN HUMAN RESOURCES 2 HR machine learning – an introduction 25 Stefan Strohmeier 3 HR machine learning on text data 46 Felix Gross 4 HR machine learning on audio and video data 68 Carmen Fernández-Martinez and Alberto Fernández 5 HR machine learning on social media data 89 Jake T. Harrison and Christopher J. Hartwell 6 HR machine learning in recruiting 105 Sven Laumer, Christian Maier, and Tim Weitzel 7 Machine learning in HR staffing 127 Florian J. Meier and Sven Laumer 8 Machine learning in personnel selection 149 Cornelius J. König and Markus Langer PART I.2 FURTHER APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCES 9 HR knowledge representation and reasoning 169 Jorge Martinez-Gil 10 HR robotic process automation 187 Peter Fettke and Stefan Strohmeier 11 HR evolutionary computing 207 Lena Wolbeck and Charlotte Köhler 12 HR natural language processing – conceptual overview and state of the art on conversational agents in human resources management 226 Sven Laumer and Stefan Morana 13 HR affective computing 243 William J. Becker, Sarah E. Tuskey, and Constant D. Beugré PART II CONSEQUENCES OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCES 14 Consequences of artificial intelligence in human resource management 261 Maarten Renkema PART III NORMATIVE ISSUES OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCES 15 Explainability of artificial intelligence in human resources 285 Markus Langer and Cornelius J. König 16 Fairness of artificial intelligence in human resources – held to a higher standard? 303 Sandra L. Fisher and Garret N. Howardson 17 Accountability of artificial intelligence in human resources 323 Katharina A. Zweig and Franziska Raudonat 18 Legitimacy of artificial intelligence in human resources – the legal framework for using artificial intelligence in human resource management 337 Kai von Lewinski and Raphael de Barros Fritz PART IV RESEARCH ISSUES OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCES 19 Design considerations for conducting artificial intelligence research in human resource management 353 Richard D. Johnson and Dianna L. Stone 20 Employing artificial intelligence in human resources research 371 Chulin Chen and Richard Landers Index 392

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Book SynopsisThis timely book presents a detailed analysis of the role of law and regulation in the utilisation of Artificial Intelligence (AI) in the media sector. As well as contributing to the wider discussion on law and AI, the book also digs deeper by exploring pressing issues at the intersections of AI, media, and the law. Chapters critically re-examine various rights and responsibilities from the perspectives of incentives for accountable utilisation of AI in the industry.Featuring chapters from leading scholars in the field, Artificial Intelligence and the Media provides a timely and in-depth research-based contribution to complex themes - especially at the interface of new technology (including AI) with media and regulation. Analysing both legislative and ethical solutions, chapters explore what “AI” and “accountability” mean in terms of media practices, principles, and power relations, as well as how to address the AI revolution with informed law and policy in order to incentivise accountable utilisation of AI and to reduce negative societal impacts.Offering ideas for further research in the area, this book is key reading for academics and researchers in the fields of information and media law, regulation, and technology law. It may also interest media law practitioners, with research-based guidance for everyday practices and tools to prepare for future developments in the area.Trade Review‘Artificial Intelligence and the Media is an urgently needed contribution to the research on AI and its impacts. While much of the scholarship so far has been field-specific, what makes this volume especially poignant is its multidisciplinary approach to the questions about the roles AI can play for media industries but also for media consumers and users as citizens, and to democracy as a whole.’ -- Minna Aslama Horowitz, University of Helsinki, Finland and St. John's University, USTable of ContentsContents List of contributors vii Introduction to Artificial Intelligence and the Media 1 Taina Pihlajarinne and Anette Alén-Savikko PART I JOURNALISTIC PRINCIPLES AND ARTIFICIAL INTELLIGENCE 1 Bias, journalistic endeavours, and the risks of artificial intelligence 8 M.R. Leiser 2 Transparency in algorithmic journalism: from ethics to law and back 33 Anette Alén-Savikko 3 The journalistic exemption in personal data processing 61 Päivi Korpisaari PART II TRUST, DISINFORMATION AND PLATFORMS 4 Social media platforms as public trustees: an approach to the disinformation problem 93 Philip M. Napoli and Fabienne Graf 5 Artificial intelligence is not a panacea: policing content on social media platforms, three dilemmas and their ethical and legal implications 123 Jingrong Tong 6 The commercial unfairness of recommender systems on social media 148 Catalina Goanta and Gerasimos Spanakis PART III REMITS AND LIMITS OF EXCLUSIVE RIGHTS 7 Creations caused by humans (or robots)? Artificial intelligence and causation requirements for copyright protection in EU law 172 Ole-Andreas Rognstad 8 Artificial intelligence and intellectual property rights: the quest or plea for artificial intelligence as a legal subject 192 Rosa Maria Ballardini and Robert van den Hoven van Genderen 9 The European copyright system as a suitable incentive for AI-based journalism? 215 Taina Pihlajarinne, Alexander Thesleff, Leo Leppänen and Sini Valmari 10 Press publishers’ right and artificial intelligence 240 Juha Vesala 11 Access to data for training algorithms in machine learning: copyright law and ‘right-stacking’ 272 Inger B. Ørstavik Conclusions on Artificial Intelligence and the Media 296 Taina Pihlajarinne and Anette Alén-Savikko Index 300

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Book SynopsisIt is almost universally agreed that consciousness and possession of a conscience are essential characteristics of human intelligence. While some believe it to be impossible to create artificial beings possessing these traits, and conclude that ultimate major goal of Artificial Intelligence is hopeless, this book demonstrates that not only is it possible to create entities with capabilities in both areas, but that they demonstrate them in ways different from our own, thereby showing a new kind of consciousness. This latter characteristic affords such entities performance beyond the reach of humans, not for lack of intelligence, but because human intelligence depends on networks of neurons which impose processing restrictions which do not apply to computers. At the beginning of the investigation of the creation of an artificial being, the main goal was not to study the possibility of whether a conscious machine would possess a conscience. However, experimental data indicate that many characteristics implemented to improve efficiency in such systems are linked to these capacities. This implies that when they are present it is because they are essential to the desired performance improvement. Moreover, since the goal is not to imitate human behavior, some of these structural characteristics are different from those displayed by the neurons of the human brain - suggesting that we are at the threshold of a new scientific field, artificial cognition, which formalizes methods for giving cognitive capabilities to artificial entities through the full use of the computational power of machines.Table of ContentsAcknowledgements ix Note on the Terminology xi Chapter 1. Presenting the Actors 1 1.1. The book 1 1.2. Human and artificial beings 4 1.3. The computer 7 1.4. The author 9 1.5. CAIA, an artificial AI scientist 11 1.6. The research domains of CAIA 15 1.7. Further reading 19 Chapter 2. Consciousness and Conscience 21 2.1. Several meanings of “consciousness” 22 2.2. Extending the meaning of “conscience” for artificial beings 25 2.3. Why is it useful to build conscious artificial beings with a conscience? 29 2.4. Towards an artificial cognition 31 2.4.1. A new kind of consciousness 32 2.4.2. A new kind of conscience 33 Chapter 3. What Does “Itself” Mean for an Artificial Being? 35 3.1. Various versions of an individual 36 3.1.1. The concept of an individual for human beings 36 3.1.2. The boundaries of an artificial being 39 3.1.3. Passive and active versions of an individual 41 3.1.4. Reflexivity 47 3.2. Variants of an individual 49 3.2.1. An individual changes with time 50 3.2.2. Learning by comparing two variants 50 3.2.3. Genetic algorithms 52 3.2.4. The bootstrap 54 3.3. Cloning artificial beings 57 3.3.1. Cloning an artificial being is easy 57 3.3.2. Cloning artificial beings is useful 58 3.4. Dr. Jekyll and Mr. Hyde 61 3.5. The Society of Mind 63 3.6. More on the subject 65 Chapter 4. Some Aspects of Consciousness 67 4.1. Six aspects of consciousness 68 4.1.1. One is in an active state 68 4.1.2. One knows what one is doing 72 4.1.3. One examines his/its internal state 80 4.1.4. One knows what one knows 84 4.1.5. One has a model of oneself 87 4.1.6. One knows that one is different from the other individuals 90 4.2. Some limits of consciousness 92 4.2.1. Some limits of consciousness for man 93 4.2.2. Some limits of consciousness for artificial beings 100 Chapter 5. Why is Auto-observation Useful? 105 5.1. Auto-observation while carrying out a task 105 5.1.1. To guide toward the solution 106 5.1.2. To avoid dangerous situations 111 5.1.3. To detect mistakes 121 5.1.4. To find where one has been clumsy 125 5.1.5. To generate a trace 126 5.2. Auto-observation after the completion of a task 129 5.2.1. Creation of an explanation 130 5.2.2. Using an explanation 133 5.2.3. Finding anomalies 138 Chapter 6. How to Observe Oneself 143 6.1. Interpreting 146 6.2. Adding supplementary orders 150 6.3. Using timed interruptions 154 6.4. Using the interruptions made by the operating system 158 6.5. Knowing its own state 159 6.6. Examining its own knowledge 160 6.7. The agents of the Society of Mind. 165 6.8. The attention 166 6.9. What is “I” 169 Chapter 7. The Conscience 173 7.1. The conscience of human beings 174 7.2. The conscience of an artificial being 179 7.3. Laws for artificial beings 183 7.3.1. Asimov’s laws of robotics 183 7.3.1. How can moral laws be implemented? 184 7.3.3. The present situation 191 Chapter 8. Implementing a Conscience 195 8.1. Why is a conscience helpful? 197 8.1.1. The conscience helps to solve problems 197 8.1.2. The conscience helps to manage its life 198 8.1.3. Two ways to define moral knowledge 199 8.1.4. Who benefits from the conscience of an artificial being? 200 8.2. The conscience of CAIA. 201 8.3. Implicit principles 202 8.4. Explicit principles 206 8.5. The consciences in a society of individuals 215 8.5.1. The Society of Mind. 216 8.5.2. Genetic algorithms 217 Chapter 9. Around the Conscience 219 9.1. Emotions 220 9.2. Changing its conscience 223 9.3. A new human conscience for our relationships with artificial beings 228 Chapter 10. What is the Future for CAIA? 237 Appendices 239 1. Constraint Satisfaction Problems 239 2. How to implement some aspects of consciousness 253 Bibliography 263 Index 269

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Book SynopsisThe energy consumption of a building has, in recent years, become a determining factor during its design and construction. With carbon footprints being a growing issue, it is important that buildings be optimized for energy conservation and CO2 reduction. This book therefore presents AI models and optimization techniques related to this application. The authors start with a review of recent models for the prediction of building energy consumption: engineering methods, statistical methods, artificial intelligence methods, ANNs and SVMs in particular. The book then focuses on SVMs, by first applying them to building energy consumption, then presenting the principles and various extensions, and SVR. The authors then move on to RDP, which they use to determine building energy faults through simulation experiments before presenting SVR model reduction methods and the benefits of parallel computing. The book then closes by presenting some of the current research and advancements in the field.Table of ContentsPreface ix Introduction xi Chapter 1. Overview of Building Energy Analysis 1 1.1. Introduction 1 1.2. Physical models 3 1.3. Gray models 6 1.4. Statistical models 6 1.5. Artificial intelligence models 8 1.5.1. Neural networks 8 1.5.2. Support vector machines 13 1.6. Comparison of existing models 14 1.7. Concluding remarks . 16 Chapter 2. Data Acquisition for Building Energy Analysis 17 2.1. Introduction 17 2.2. Surveys or questionnaires 18 2.3. Measurements 21 2.4. Simulation 25 2.4.1. Simulation software 26 2.4.2. Simulation process 28 2.5. Data uncertainty 34 2.6. Calibration 35 2.7. Concluding remarks 37 Chapter 3. Artificial Intelligence Models 39 3.1. Introduction 39 3.2. Artificial neural networks 40 3.2.1. Single-layer perceptron 41 3.2.2. Feed forward neural network 43 3.2.3. Radial basis functions network 44 3.2.4. Recurrent neural network 47 3.2.5. Recursive deterministic perceptron 49 3.2.6. Applications of neural networks 51 3.3. Support vector machines 53 3.3.1. Support vector classification 54 3.3.2. ε-support vector regression 59 3.3.3. One-class support vector machines 62 3.3.4. Multiclass support vector machines 63 3.3.5. v-support vector machines 64 3.3.6. Transductive support vector machines 65 3.3.7. Quadratic problem solvers . 67 3.3.8. Applications of support vector machines 75 3.4. Concluding remarks 76 Chapter 4. Artificial Intelligence for Building Energy Analysis 79 4.1. Introduction 79 4.2. Support vector machines for building energy prediction 80 4.2.1. Energy prediction definition 80 4.2.2. Practical issues 81 4.2.3. Support vector machines for prediction 85 4.3. Neural networks for fault detection and diagnosis 91 4.3.1. Description of faults 94 4.3.2. RDP in fault detection 95 4.3.3. RDP in fault diagnosis 100 4.4. Concluding remarks 102 Chapter 5. Model Reduction for Support Vector Machines 103 5.1. Introduction 103 5.2. Overview of model reduction 104 5.2.1. Wrapper methods 105 5.2.2. Filter methods 106 5.2.3. Embedded methods 107 5.3. Model reduction for energy consumption 108 5.3.1. Introduction 108 5.3.2. Algorithm 109 5.3.3. Feature set description 111 5.4. Model reduction for single building energy 112 5.4.1. Feature set selection 112 5.4.2. Evaluation in experiments 114 5.5. Model reduction for multiple buildings energy 116 5.6. Concluding remarks 119 Chapter 6. Parallel Computing for Support Vector Machines 121 6.1. Introduction 121 6.2. Overview of parallel support vector machines 122 6.3. Parallel quadratic problem solver 123 6.4. MPI-based parallel support vector machines 127 6.4.1. Message passing interface programming model 127 6.4.2. Pisvm 129 6.4.3. Psvm 130 6.5. MapReduce-based parallel support vector machines 130 6.5.1. MapReduce programming model 131 6.5.2. Caching technique 133 6.5.3. Sparse data representation 133 6.5.4. Comparison of MRPsvm with Pisvm 134 6.6. MapReduce-based parallel ε-support vector regression 138 6.6.1. Implementation aspects 138 6.6.2. Energy consumption datasets 139 6.6.3. Evaluation for building energy prediction 140 6.7. Concluding remarks 142 Summary and Future of Building Energy Analysis 145 Bibliography 149 Index 163

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Book SynopsisInformation and communication technologies have increased their share of services in contemporary economic exchanges. We are witnessing a transformation of modern economies characterized by a predominant role of information and knowledge in the production of wealth. In order to make this intangible resource bear fruit, organizations are looking for ways, methods, procedures, processes and technical solutions to efficiently manage knowledge Within a framework of research into synergies and resource interdependence, organizations also rely on strategic alliances (joint venture), mergers or other legal forms of association that have an impact on knowledge management. This book explores the range of knowledge management techniques.Table of ContentsIntroduction ix Part 1 A Systemic Approach to the Organization Based on Knowledge Management and its Tools 1 Chapter 1 Theoretical Anchoring of Knowledge 3 1.1 Individual knowledge and skills 4 1.2 From individual learning to organizational learning 9 1.3 Knowledge management 16 1.4 Knowledge management systems 23 1.5 Communities, teams and knowledge management 26 1.6 Knowledge management and cultures 31 Chapter 2 The Design of the Learning Organization 37 2.1 From the systemic approach to the organizational design 37 2.2 Proposal of an organizational design for knowledge management: "learning organization design" 44 Part 2 Emergence of a New Design: that of the Learning Organization 61 Chapter 3 Real-World Access Methodology 63 3.1 Methodological choices 64 3.2 The field of research 67 3.3 Data collection 85 3.4 Processing of the collected data 92 Chapter 4 Case Study 99 4.1 Design of the learning organization SCCC (before the merger) 99 4.2 Design of the learning organization SCCC (period of merger with N) 108 4.3 Design of the learning organization NSN (post-merger) 114 4.4 Overview of the three phases 122 Chapter 5 Emergence of a New Organizational Design 127 5.1 Emergence of a design of the learning organization 127 5.2 Emergence of a new organizational design in view of the impact of culture 130 5.3 Emergence of a new organizational design when faced with knowledge boundaries 131 Conclusion 139 Bibliography 147 Index 171

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Book SynopsisThe idea of autonomous systems that are able to make choices according to properties which allow them to experience, apprehend and assess their environment is becoming a reality. These systems are capable of auto-configuration and self-organization. This book presents a model for the creation of autonomous systems based on a complex substratum, made up of multiple electronic components that deploy a variety of specific features. This substratum consists of multi-agent systems which act continuously and autonomously to collect information from the environment which they then feed into the global system, allowing it to generate discerning and concrete representations of its surroundings. These systems are able to construct a so-called artificial corporeity which allows them to have a sense of self, to then behave autonomously, in a way reminiscent of living organisms.Table of ContentsIntroduction ix List of Algorithms xi Chapter 1 Systems and their Design 1 1.1 Modeling systems 1 1.1.1 Conventional systems 2 1.1.2 Complex systems 3 1.1.3 System of systems 3 1.2 Autonomous systems 5 1.3 Agents and multi-agent systems 6 1.3.1 The weak notion of agent 7 1.3.2 The strong notion of agent 7 1.3.3 Cognitive agents and reactive agents 8 1.3.4 Multi-agent systems 9 1.3.5 Reactive agent-based MAS 10 1.3.6 Cognitive agent-based MAS 11 1.4 Systems and organisms 13 1.5 The issue of modeling an autonomous system 13 Chapter 2 The Global Architecture of an Autonomous System 17 2.1 Introduction 17 2.2 Reactivity of a system 17 2.3 The basic structure of an autonomous system: the substratum 18 2.3.1 A detailed example: smoothing the flow or urban traffic 20 2.4 The membrane of autonomous systems 22 2.4.1 Membrane and information 25 2.5 Two types of proactivity and the notion of artificial organ 26 2.5.1 Weak proactivity 26 2.5.2 Strong proactivity 27 2.5.3 Measuring proactivity with dynamic graphs 30 2.6 Autonomy and current representation 31 2.6.1 Current representation in an autonomous system 32 2.7 The unifying system that generates representations 33 Chapter 3 Designing a Multi-agent Autonomous System 41 3.1 Introduction 41 3.2 The object layer on the substratum 41 3.3 The agent representation of the substratum: interface agents, organs and the notion of sensitivity 44 3.3.1 Artificial organs 46 3.3.2 Sensitivity of the corporeity 47 3.4 The interpretation system and the conception agents 47 3.4.1 The properties of a conception agent in the interpretation system 49 3.4.2 An example 52 3.4.3 Creating a conception agent 57 3.5 Aggregates of conception agents 58 3.6 The intent and the activity of conception agents 60 3.7 Agentifying conception agents 63 3.8 Activity of a conception agent 65 3.9 The three layers of conceptual agentification and the role of control 70 3.9.1 First guiding principle for the architecture of an autonomous system 74 3.10 Semantic lattices and the emergence of representations in the interpretation system 77 3.11 The general architecture of the interpretation system 84 3.12 Agentification of knowledge and organizational memory 86 3.13 Setting up the membrane network of an autonomous system 94 3.14 Behavioral learning of the autonomous system 96 Chapter 4 Generation of Current Representation and Tendencies 105 4.1 Introduction 105 4.2 Generation of current representation and semantic lattices 105 4.2.1 Openness and deployment: major properties of autonomous systems 106 4.2.2 Incentive-based control and evaluation agents 107 4.2.3 Evaluation agents’ access to organizational memory 110 4.2.4 The role of evaluation agents in the extracted lattice 110 4.2.5 The notion of dynamic lattices 110 4.2.6 Algorithms for generating representations 111 4.2.7 Mathematical interpretation 115 4.3 The cause leading the system to choose a concrete intent 116 4.3.1 Determination of intent 118 4.3.2 Intent and tendencies 120 4.4 Presentation of artificial tendencies 123 4.5 Algorithm for the generation of a stream of representations under tendencies 134 Chapter 5 The Notions of Point of View, Intent and Organizational Memory 137 5.1 Introduction 137 5.2 The notion of point of view in the generation of representations 137 5.3 Three organizational principles of the interpretation system for leading the intent 144 5.3.1 Principle of continuity engagement 145 5.3.2 The bifurcation principle 146 5.3.3 The principle of necessary reason and reliability 147 5.4 Algorithms for intent decisions 147 5.6 Organizational memory and the representation of artificial life experiences 151 5.7 Effective autonomy and the role of the modulation component 156 5.8 Degree of organizational freedom 159 Chapter 6 Towards the Minimal Self of an Autonomous System 161 6.1 Introduction 161 6.2 The need for tendencies when leading the system 161 6.3 Needs and desires of the autonomous system 164 6.4 A scaled-down autonomous system: the artificial proto-self 168 6.5 The internal choice of expressed tendencies and the minimal self 171 6.6 The incentive to produce representations 176 6.7 Minimal self affectivity: emotions and sensations 179 6.8 Algorithms for tendency activation 182 6.9 The feeling of generating representations 188 Chapter 7 Global Autonomy of Distributed Autonomous Systems 197 7.1 Introduction 197 7.2 Enhancement of an autonomous system by itself 197 7.3 Communication among autonomous systems in view of their union 201 7.4 The autonomous meta-system composed of autonomous systems 204 7.5 The system generating autonomous systems: the meta-level of artificial living 207 Conclusion 211 Bibliography 213 Index 215

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Book SynopsisThis important book presents new and original work at the frontiers of economics - namely the interface between artificial intelligence (AI) and neoclassical economics.Artificial Intelligence and Economic Analysis focuses on three quite distinct lines of AI orientated research in economics: applications intended to extend neoclassical theory, applications intended to undermine neoclassical theory and applications which ignore neoclassical theory in the quest for new modelling techniques and fields of analysis. The contributors - all of whom are well established in the field - seek to identify those areas where the science of artificial intelligence could enrich standard economic analysis. It includes material from mainstream economists who are willing to express their own views about the limits of mainstream economic modelling and AI based economic modelling.The book makes an important contribution to a new and exciting area of economics which holds much hope for the future.Trade Review'It provides interesting reading and a source of speculation for those who hope to find uses for AI techniques in economic research.' -- Kent D. Wall, Journal of Economic Behavior & OrganizationTable of ContentsContents: 1. Introduction (S. Moss) 2. Economics and Intelligence (R. Marris) 3. Artificial Intelligence Models of Complex Economic Systems (S. Moss) 4. AI Modelling Techniques: The Emergence of a Supportive Framework for Modelling Complex Behaviour in Economics (J. Rae and M.L. Reynolds) 5. Strategic Decision Making: Orthodox Theory Versus Artificial Intelligence Approaches (A. and S. Moss) 6. Experiments, Games and Economics (J.D. Hey and M.L. Reynolds) 7. Artificial Intelligence and the Economics of Technological Change (P. Stoneman) 8. Some Thoughts on Economic Theory and Artificial Intelligence (H. Dixon) 9. Church’s Thesis and Game Theory: An Overview of Some Results (L. Anderlini) 10. The Development of Intelligent Macroeconometric Models and Modelling Procedures (M. Artis, S. Moss and P. Ormerod) Bibliography Index

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Book SynopsisMarshall Goldsmith wrote in his book, What Got You Here, Won’t Get You There, that people rely on their past experience to address new challenges. The limitation with this approach is that these new challenges often arise from different contexts and may not be susceptible to traditional approaches.In the coming era of artificial intelligence (AI), expanded use of robots, and increased trans-national commerce, humanity will face monumental challenges that will differ from those we have faced in the past, including how to avoid mass unemployment due to rapid growth of automation. In order to survive and thrive in this new era, we will have to think and act differently, so that new ideas can solve not only the problems of the present but also of the near and distant future.Economic Renaissance in the Age of Artificial Intelligence explores a wide range of new approaches to the economic, social, legal, scientific, technological, financial, architectural, environmental, and humanistic challenges that humanity will face due to increased automation. The new methods and approaches outlined by the various experts in this book will help inform and inspire humanity to create a more balanced world in which science, economics, and the environment can thrive for years to come.

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Book SynopsisIn today’s high-pressured world, digital transformation is everywhere on the agendas of corporate boards and has risen to the top of CEOs’ strategic plans. Artificial intelligence, blockchain, 3D printing, the Internet of Things, and drones are some of the emerging technologies that are already transforming our world. In this fast changing domain— predicted by few and now reality for all how can companies transform today’s challenges into tomorrow’s opportunities?This book is targeted to help a broad audience such as students, professionals, business, and technology managers to transform an old-world brick and mortar organization to a new-world digital leader. The author addresses various questions including: what essential components does digital transformation include, and how does it impact the enterprise? How does convergence of emerging technologies benefit your organization? How can you start transformation and technology planning projects?

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Book SynopsisThis world first summary of the evolution of 2D chatbots in websites, backends of portals and social media apps, and conversationally advanced 3D mixed reality cognitive interfaces, serves several purposes.It dissects some of the best-known case studies to emerge from the past two decades of tech giants launching the best chatbot, or supposedly the smartest, intelligent virtual assistant. From Microsoft’s Tay.ai to London’s Eugene Goostman claim to turing test fame, from the market dominating Amazon Alexa to Gatebox’s IoT innovation with its multi-cloned Japanese hologram girlfriend, this is the first ever history of bots.This book also touches on the Trump vs Clinton chatbot wars as well as the UK Labour Party’s dating site stunt, including references made to Facebook Messenger bots and the impact of the Cambridge Analytica scandal. Included in the book is a hands-on checklist and guidelines in for people wanting to buy or license bots for their companies and organizations. The author also outlines the possible use cases and key issues to consider when sourcing and commissioning your first botification project, with the fi nal chapters predicting where the future development – and development traps – might lie.In this easy-to-read overview, Tania Peitzker cites leading business intelligence and analyst firms’ research, and takes a deeper dive into the practical challenges of chatbots, including the obstacles and triumphs experienced by business chatbots.

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Book SynopsisThe field of multimedia is unique in offering a rich and dynamic forum for researchers from “traditional” fields to collaborate and develop new solutions and knowledge that transcend the boundaries of individual disciplines. Despite the prolific research activities and outcomes, however, few efforts have been made to develop books that serve as an introduction to the rich spectrum of topics covered by this broad field. A few books are available that either focus on specific subfields or basic background in multimedia. Tutorial-style materials covering the active topics being pursued by the leading researchers at frontiers of the field are currently lacking.In 2015, ACM SIGMM, the special interest group on multimedia, launched a new initiative to address this void by selecting and inviting 12 rising-star speakers from different subfields of multimedia research to deliver plenary tutorial-style talks at the ACM Multimedia conference for 2015. Each speaker discussed the challenges and state-of-the-art developments of their prospective research areas in a general manner to the broad community. The covered topics were comprehensive, including multimedia content understanding, multimodal human-human and human-computer interaction, multimedia social media, and multimedia system architecture and deployment.Following the very positive responses to these talks, the speakers were invited to expand the content covered in their talks into chapters that can be used as reference material for researchers, students, and practitioners. Each chapter discusses the problems, technical challenges, state-of-the-art approaches and performances, open issues, and promising direction for future work. Collectively, the chapters provide an excellent sampling of major topics addressed by the community as a whole. This book, capturing some of the outcomes of such efforts, is well positioned to fill the aforementioned needs in providing tutorial-style reference materials for frontier topics in multimedia.At the same time, the speed and sophistication required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming common. And in addition to batch processing, streaming analysis of real-time data is required to let organizations take timely action. Future computing platforms will need to not only scale out traditional workloads, but support these new applications too.This book, a revised version of the 2014 ACM Dissertation Award winning dissertation, proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance. And whereas most deployed systems only support simple one-pass computations (e.g., SQL queries), ours also extends to the multi-pass algorithms required for complex analytics like machine learning. Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing.We achieve these results through a simple extension to MapReduce that adds primitives for data sharing, called Resilient Distributed Datasets (RDDs). We show that this is enough to capture a wide range of workloads. We implement RDDs in the open source Spark system, which we evaluate using synthetic and real workloads. Spark matches or exceeds the performance of specialized systems in many domains, while offering stronger fault tolerance properties and allowing these workloads to be combined. Finally, we examine the generality of RDDs from both a theoretical modeling perspective and a systems perspective.This version of the dissertation makes corrections throughout the text and adds a new section on the evolution of Apache Spark in industry since 2014. In addition, editing, formatting, and links for the references have been added.Table of Contents Preface PART I: MULTIMEDIA CONTENT ANALYSIS 1. Deep Learning for Video Classification and Captioning 2. Audition for Multimedia Computing 3. Multimodal Analysis of Free-standing Conversational Groups 4. Encrypted Domain Multimedia Content Analysis 5. Efficient Similarity Search PART II: HUMAN-CENTERED MULTIMEDIA COMPUTING 6. Social-Sensed Multimedia Computing 7. Situation Recognition Using Multimodal Data 8. Hawkes Processes for Events in Social Media 9. Utilizing Implicit User Cues for Multimedia Analytics PART III: MULTIMEDIA COMMUNICATION AND SYSTEMS 10. Multimedia Fog Computing: Minions in the Cloud and Crowd 11. Cloud Gaming Bibliography Index Editor Biography

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Book SynopsisThe field of multimedia is unique in offering a rich and dynamic forum for researchers from “traditional” fields to collaborate and develop new solutions and knowledge that transcend the boundaries of individual disciplines. Despite the prolific research activities and outcomes, however, few efforts have been made to develop books that serve as an introduction to the rich spectrum of topics covered by this broad field. A few books are available that either focus on specific subfields or basic background in multimedia. Tutorial-style materials covering the active topics being pursued by the leading researchers at frontiers of the field are currently lacking.In 2015, ACM SIGMM, the special interest group on multimedia, launched a new initiative to address this void by selecting and inviting 12 rising-star speakers from different subfields of multimedia research to deliver plenary tutorial-style talks at the ACM Multimedia conference for 2015. Each speaker discussed the challenges and state-of-the-art developments of their prospective research areas in a general manner to the broad community. The covered topics were comprehensive, including multimedia content understanding, multimodal human-human and human-computer interaction, multimedia social media, and multimedia system architecture and deployment.Following the very positive responses to these talks, the speakers were invited to expand the content covered in their talks into chapters that can be used as reference material for researchers, students, and practitioners. Each chapter discusses the problems, technical challenges, state-of-the-art approaches and performances, open issues, and promising direction for future work. Collectively, the chapters provide an excellent sampling of major topics addressed by the community as a whole. This book, capturing some of the outcomes of such efforts, is well positioned to fill the aforementioned needs in providing tutorial-style reference materials for frontier topics in multimedia.At the same time, the speed and sophistication required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming common. And in addition to batch processing, streaming analysis of real-time data is required to let organizations take timely action. Future computing platforms will need to not only scale out traditional workloads, but support these new applications too.This book, a revised version of the 2014 ACM Dissertation Award winning dissertation, proposes an architecture for cluster computing systems that can tackle emerging data processing workloads at scale. Whereas early cluster computing systems, like MapReduce, handled batch processing, our architecture also enables streaming and interactive queries, while keeping MapReduce's scalability and fault tolerance. And whereas most deployed systems only support simple one-pass computations (e.g., SQL queries), ours also extends to the multi-pass algorithms required for complex analytics like machine learning. Finally, unlike the specialized systems proposed for some of these workloads, our architecture allows these computations to be combined, enabling rich new applications that intermix, for example, streaming and batch processing.We achieve these results through a simple extension to MapReduce that adds primitives for data sharing, called Resilient Distributed Datasets (RDDs). We show that this is enough to capture a wide range of workloads. We implement RDDs in the open source Spark system, which we evaluate using synthetic and real workloads. Spark matches or exceeds the performance of specialized systems in many domains, while offering stronger fault tolerance properties and allowing these workloads to be combined. Finally, we examine the generality of RDDs from both a theoretical modeling perspective and a systems perspective.This version of the dissertation makes corrections throughout the text and adds a new section on the evolution of Apache Spark in industry since 2014. In addition, editing, formatting, and links for the references have been added.Table of Contents Preface PART I: MULTIMEDIA CONTENT ANALYSIS 1. Deep Learning for Video Classification and Captioning 2. Audition for Multimedia Computing 3. Multimodal Analysis of Free-standing Conversational Groups 4. Encrypted Domain Multimedia Content Analysis 5. Efficient Similarity Search PART II: HUMAN-CENTERED MULTIMEDIA COMPUTING 6. Social-Sensed Multimedia Computing 7. Situation Recognition Using Multimodal Data 8. Hawkes Processes for Events in Social Media 9. Utilizing Implicit User Cues for Multimedia Analytics PART III: MULTIMEDIA COMMUNICATION AND SYSTEMS 10. Multimedia Fog Computing: Minions in the Cloud and Crowd 11. Cloud Gaming Bibliography Index Editor Biography

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Book SynopsisCet ouvrage collectif s’inscrit dans le cadre des travaux de la Chaire justice sociale et intelligence artificielle Abeona-ENS-OBVIA. Il propose une réflexion multidisciplinaire sur les enjeux des usages de l’intelligence artificielle, mais surtout à partir d’une perspective de justice sociale. Le concept de justice sociale permet d’inclure des dimensions, principalement saisies par les sciences sociales et humaines, et qui ne sont pas traditionnellement associées aux technologies d’intelligence artificielle. Cela permet alors d’appréhender des dimensions telles que la justice et l’équité, mais aussi la solidarité ou encore la dignité ; ces dimensions constituent de puissants outils de changement social lorsqu’ils sont mobilisés par différents acteurs. Les contributions de cet ouvrage mettent en évidence des réflexions quant à la mise en place de conditions sociétales et de pistes d’action pour un déploiement des technologies d’intelligence artificielle en respect des sociétés humaines.This collective book is part of the work of the Abeona-ENS-OBVIA Chair in Social Justice and Artificial Intelligence. It proposes a multidisciplinary reflection on the challenges posed by the uses of artificial intelligence, above all from a social justice perspective.The concept of social justice allows to include dimensions that are not traditionally associated with artificial intelligence technologies, and which are primarily addressed by the social sciences and humanities. This makes it possible to comprehend dimensions such as justice and equity, but also solidarity and dignity; these dimensions constitute powerful tools for social change when mobilized by different actors.The contributions in this book highlight reflections on the definition of societal conditions and avenues of action for artificial intelligence technologies deployment in respect for human societies.

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Book SynopsisAs voice interfaces and virtual assistants have moved out of the industry research labs and into the pockets, desktops and living rooms of the general public, a demand for a new kind of user experience (UX) design is emerging. Although the people are becoming familiar with Siri, Alexa, Cortana and others, their user experience is still characterized by short, command- or query-oriented exchanges, rather than longer, conversational ones. Limitations of the microphone and natural language processing technologies are only part of the problem. Current conventions of UX design apply mostly to visual user interfaces, such as web or mobile; they are less useful for deciding how to organize utterances, by the user and the virtual agent, into sequences that work like those of natural human conversation. This edited book explores the intersection of UX design, of both text- or voice-based virtual agents, and the analysis of naturally occurring human conversation (e.g., the Conversation Analysis, Discourse Analysis and Interactional Sociolinguistics literatures). It contains contributions from researchers, from academia and industry, with varied backgrounds working in the area of human-computer interaction. Each chapter explores some aspect of conversational UX design. Some describe the design challenges faced in creating a particular virtual agent. Others discuss how the findings from the literatures of the social sciences can inform a new kind of UX design that starts with conversation.Table of ContentsConversational UX Design: An Introduction.- Adapting to Customer Initiative: Insights from Human Service Encounters.- Safety First: Conversational agents for Health Care.- Conversational Agents for Physical World Navigation.- Helping Users Reflect on Their Own Heath-related Behaviors.- Teaching Agents When they Fail: End User Development in Goal-oriented Conversational Agents.- Recovering from Dialogue Failures Using Multiple Agents in Wealth Management Advice.- Conversational Style: Beyond the nuts and bolts of conversation.- A natural Conversation Framework for Conventional UX Design.

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Book SynopsisThis book presents innovative ideas, cutting-edge findings, and novel techniques, methods, and applications in a broad range of cybersecurity and cyberthreat intelligence areas. As our society becomes smarter, there is a corresponding need to be able to secure our cyberfuture. The approaches and findings described in this book are of interest to businesses and governments seeking to secure our data and underpin infrastructures, as well as to individual users.

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Book SynopsisIs it possible to energise the reuse of urban abandoned spaces with low financial capital investment? Addressing this question requires a normative and cultural change, where the rules are less focused on the material processes of producing space and more aimed at fostering the construction of relationships. The reality of several European cities shows how traditional forms of stimulating urban renewal – with respect to the financing of operations, how to design and build, and urban planning legislation – no longer work. This book examines an alternative culture of design and regulation, drawing on the richness of the various approaches to the subject to present an integrated study of the phenomenon of reuse across its economic, architectural and urban dimensions. From this theoretical base, it empirically analyses six Italian case studies in terms of the broadness of geography and in their governance models, and of the important role of the unity of cultural destination for their reuse proposal. The book is intended for all those involved in the cultural challenge of reusing urban abandoned spaces, including public administrators, entrepreneurs, architects, planners and academics. Table of ContentsVacant Buildings. Distinguishing Heterogeneous Cases: Public Items Vs. Private Items; Empty Properties Vs. Abandoned Properties.- Participation, Culture, Entrepreneurship: Using Public Real Estate Assets to Create New Urban Regeneration Models.- Intensity of Uses and Spatial Devices.- The Appraisal Challenge in Cultural Urban Regeneration: A Proposal of An Evaluation Procedure.- Theoretical Basis and Design of Analysis.- The Case Study Profiles.- Governance, Economic Sustainability and Socio-Spatial Relationships.- Shapes, Rules and Value.

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