Search results for ""Author Simon Lindgren""

We live in an age of artificial intelligence. Machines think and act in ever more complex ways, making suggestions and decisions on our behalf. While AI might be seen as practical and profitable, issues of data surveillance, algorithmic control, and sexist and racist bias persist. In this rapidly changing landscape, social analysis of AI risks getting scaled down to issues of ‘ethics’, ‘responsibility’, and ‘fairness’. While these are important issues, they must be addressed not from an ‘AI first’ perspective, but more thoroughly in terms of power and contention. Approaching artificial intelligence from the often overlooked perspective of critical social theory, this book provides a much-needed intervention on how both old and new theories conceptualize the social consequences of AI. Questions are posed about the ideologies driving AI, the mythologies surrounding AI, and the complex relationship between AI and power. Simon Lindgren provides a way of defining AI as an object of social and political critique, and guides the reader through a set of contentious areas where AI and politics intersect. In relation to these topics, critical theories are drawn upon, both as an argument for and an illustration of how AI can be critiqued. Given the opportunities and challenges of AI, this book is a must-read for students and scholars in the humanities, social sciences, and STEM disciplines.

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The datafication of our world offers huge challenges and opportunities for social science. The ‘data-drivenness’ of computational research can occur at the expense of theoretical reflection and interpretation. Additionally, it can be difficult to reconcile the ‘quantitative’ dimensions of big data with the ‘qualitative’ sensibilities needed for its understanding. At the same time, this opens up possibilities for reimagining key principles of social inquiry. In this experimental and provocative book, Simon Lindgren argues that a hybrid approach to data and theory must be developed in order to make sense of today's ambivalent, turbulent, and media-saturated political landscape. He pushes for the development of a critical science of data, joining the interpretive theoretical and ethical sensibilities of social science with the predictive and prognostic powers of data science and computational methods. In order for theories and research methods to be more useful and relevant, they must be dismantled and put together in new, alternative, and unexpected ways. Data Theory is essential reading for social scientists and data scientists, as well as students taking courses in social theory and data, digital methods, big data, and data and society.

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We live in an age of artificial intelligence. Machines think and act in ever more complex ways, making suggestions and decisions on our behalf. While AI might be seen as practical and profitable, issues of data surveillance, algorithmic control, and sexist and racist bias persist. In this rapidly changing landscape, social analysis of AI risks getting scaled down to issues of ‘ethics’, ‘responsibility’, and ‘fairness’. While these are important issues, they must be addressed not from an ‘AI first’ perspective, but more thoroughly in terms of power and contention. Approaching artificial intelligence from the often overlooked perspective of critical social theory, this book provides a much-needed intervention on how both old and new theories conceptualize the social consequences of AI. Questions are posed about the ideologies driving AI, the mythologies surrounding AI, and the complex relationship between AI and power. Simon Lindgren provides a way of defining AI as an object of social and political critique, and guides the reader through a set of contentious areas where AI and politics intersect. In relation to these topics, critical theories are drawn upon, both as an argument for and an illustration of how AI can be critiqued. Given the opportunities and challenges of AI, this book is a must-read for students and scholars in the humanities, social sciences, and STEM disciplines.

Read more less

The datafication of our world offers huge challenges and opportunities for social science. The ‘data-drivenness’ of computational research can occur at the expense of theoretical reflection and interpretation. Additionally, it can be difficult to reconcile the ‘quantitative’ dimensions of big data with the ‘qualitative’ sensibilities needed for its understanding. At the same time, this opens up possibilities for reimagining key principles of social inquiry. In this experimental and provocative book, Simon Lindgren argues that a hybrid approach to data and theory must be developed in order to make sense of today's ambivalent, turbulent, and media-saturated political landscape. He pushes for the development of a critical science of data, joining the interpretive theoretical and ethical sensibilities of social science with the predictive and prognostic powers of data science and computational methods. In order for theories and research methods to be more useful and relevant, they must be dismantled and put together in new, alternative, and unexpected ways. Data Theory is essential reading for social scientists and data scientists, as well as students taking courses in social theory and data, digital methods, big data, and data and society.

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As 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.

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