{"product_id":"data-conscience-9781119821182","title":"Data Conscience","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eDATA CONSCIENCE ALGORITHMIC S1EGE ON OUR HUM4N1TY EXPLORE HOW D4TA STRUCTURES C4N HELP OR H1NDER SOC1AL EQU1TY   Data has enjoyed bystander' status as we've attempted to digitize responsibility and morality in tech. In fact, data's importance should earn it a spot at the center of our thinking and strategy around building a better, more ethical world. It's useand misuselies at the heart of many of the racist, gendered, classist, and otherwise oppressive practices of modern tech.  In Data Conscience: Algorithmic Siege on our Humanity, computer science and data inclusivity thought leader Dr. Brandeis Hill Marshall delivers a call to action for rebel tech leaders, who acknowledge and are prepared to address the current limitations of software development. In the book, Dr. Brandeis Hill Marshall discusses how the philosophy of move fast and break things is, itself, broken, and requires change.  You'll learn about the ways that discrimination rears its ugly head in the digital data space and how to address them with several known algorithms, including social network analysis, and linear regression  A can't-miss resource for junior-level to senior-level software developers who have gotten their hands dirty with at least a handful of significant software development projects, Data Conscience also provides readers with:  Discussions of the importance of transparencyExplorations of computational thinking in practiceStrategies for encouraging accountability in techWays to avoid double-edged data visualizationSchemes for governing data structures with law and algorithms\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForeword xix\u003c\/p\u003e \u003cp\u003eIntroduction xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Transparency 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Oppression By. . . 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Law 4\u003c\/p\u003e \u003cp\u003eSlave Codes 5\u003c\/p\u003e \u003cp\u003eBlack Codes 5\u003c\/p\u003e \u003cp\u003eThe Rise of Jim Crow Laws 8\u003c\/p\u003e \u003cp\u003eBreaking Open Jim Crow Laws 11\u003c\/p\u003e \u003cp\u003eOvert Surveillance 12\u003c\/p\u003e \u003cp\u003eSurveillance at Scale 13\u003c\/p\u003e \u003cp\u003eThe Science 16\u003c\/p\u003e \u003cp\u003eNumbers 16\u003c\/p\u003e \u003cp\u003eAnthropometry 18\u003c\/p\u003e \u003cp\u003eEugenics 19\u003c\/p\u003e \u003cp\u003eSummary 23\u003c\/p\u003e \u003cp\u003eNotes 23\u003c\/p\u003e \u003cp\u003eRecommended Reading 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Morality 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Is All Around Us 29\u003c\/p\u003e \u003cp\u003eMorality and Technology 33\u003c\/p\u003e \u003cp\u003eDefining Tech Ethics 33\u003c\/p\u003e \u003cp\u003eMapping Tech Ethics to Human Ethics 39\u003c\/p\u003e \u003cp\u003eSqueezing in Data Ethics 45\u003c\/p\u003e \u003cp\u003eMisconceptions of Data Ethics 49\u003c\/p\u003e \u003cp\u003eMisconception 1: Goodness of Data, and\u003c\/p\u003e \u003cp\u003eTech by Proxy, Is Apolitical or Bipartisan 49\u003c\/p\u003e \u003cp\u003eMisconception 2: Data Ethics Is Focused Solely on Laws Protecting Confidentiality and Privacy 50\u003c\/p\u003e \u003cp\u003eMisconception 3: Implementing Data Ethics Practices Will Make Data Objective 52\u003c\/p\u003e \u003cp\u003eNotable Misconception Mentions: Ethics and Diversity, Equity, and Inclusion (DEI) Are Interchangeable 53\u003c\/p\u003e \u003cp\u003eAnother Notable Mention: Software Developers Are Only Responsible for Societal Outcomes Stemming from Their Code 54\u003c\/p\u003e \u003cp\u003eLimits of Tech and Data Ethics 55\u003c\/p\u003e \u003cp\u003eSummary 57\u003c\/p\u003e \u003cp\u003eNotes 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Bias 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTypes of Bias 62\u003c\/p\u003e \u003cp\u003eDefining Bias 63\u003c\/p\u003e \u003cp\u003eConcrete Example of Biases 65\u003c\/p\u003e \u003cp\u003eThe Bias Wheel 70\u003c\/p\u003e \u003cp\u003eBefore You Code 73\u003c\/p\u003e \u003cp\u003eCase Study Scenario: Data Sourcing for an Employee Candidate Résumé Database 77\u003c\/p\u003e \u003cp\u003eCase Study Scenario: Data Manipulation for an Employee Candidate Résumé Database 78\u003c\/p\u003e \u003cp\u003eCase Study Scenario: Data Interpretation for an Employee\u003c\/p\u003e \u003cp\u003eCandidate Résumé Database 82\u003c\/p\u003e \u003cp\u003eBias Messaging 83\u003c\/p\u003e \u003cp\u003eSummary 83\u003c\/p\u003e \u003cp\u003eNotes 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Computational Thinking in Practice 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReady to Code 88\u003c\/p\u003e \u003cp\u003eThe Shampoo Algorithm 89\u003c\/p\u003e \u003cp\u003eComputational Thinking 91\u003c\/p\u003e \u003cp\u003eCoding Environments 93\u003c\/p\u003e \u003cp\u003eAlgorithmic Justice Practice 95\u003c\/p\u003e \u003cp\u003eCode Cloning 97\u003c\/p\u003e \u003cp\u003eSocio-Techno-Ethical Review: \u003ci\u003eapp.py \u003c\/i\u003e101\u003c\/p\u003e \u003cp\u003eSocio-Techno-Ethical Review: \u003ci\u003escreen.py \u003c\/i\u003e103\u003c\/p\u003e \u003cp\u003eSocio-Techno-Ethical Review: \u003ci\u003esearch.py \u003c\/i\u003e109\u003c\/p\u003e \u003cp\u003eSummary 114\u003c\/p\u003e \u003cp\u003eNotes 114\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Accountability 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Messy Gathering Grove 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAsk the Why Question 120\u003c\/p\u003e \u003cp\u003eCollection 124\u003c\/p\u003e \u003cp\u003eOpen Source Dataset Example: Deciding Data Ownership 127\u003c\/p\u003e \u003cp\u003eOpen Source Dataset Example: Considering Data Privacy 129\u003c\/p\u003e \u003cp\u003eReformat 133\u003c\/p\u003e \u003cp\u003eSummary 139\u003c\/p\u003e \u003cp\u003eNotes 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Inconsistent Storage Sanctuary 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAsk the “What” Question 144\u003c\/p\u003e \u003cp\u003eFiles, Sheets, and the Cloud 146\u003c\/p\u003e \u003cp\u003eDecisions in a Vacuum 149\u003c\/p\u003e \u003cp\u003eCase Study: Black Twitter 150\u003c\/p\u003e \u003cp\u003eModeling Content Associations 153\u003c\/p\u003e \u003cp\u003eManipulating with SQL 158\u003c\/p\u003e \u003cp\u003eSummary 160\u003c\/p\u003e \u003cp\u003eNotes 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Circus of Misguided Analysis 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAsk the “How” Question 164\u003c\/p\u003e \u003cp\u003eMisevaluating the “Cleaned” Dataset 169\u003c\/p\u003e \u003cp\u003eOverautomating k, K, and Thresholds 177\u003c\/p\u003e \u003cp\u003eDeepfake Technology 179\u003c\/p\u003e \u003cp\u003eNot Estimating Algorithmic Risk at Scale 185\u003c\/p\u003e \u003cp\u003eSummary 187\u003c\/p\u003e \u003cp\u003eNotes 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Double-Edged Visualization Sword 191\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAsk the “When” Question 192\u003c\/p\u003e \u003cp\u003eCritiquing Visual Construction 197\u003c\/p\u003e \u003cp\u003eDisabilities in View 201\u003c\/p\u003e \u003cp\u003ePretty Picture Mirage 204\u003c\/p\u003e \u003cp\u003eCase Study: SAT College Board Dataset 207\u003c\/p\u003e \u003cp\u003eSummary 208\u003c\/p\u003e \u003cp\u003eNotes 209\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Governance 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 By the Law 215\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFederal and State Legislation 216\u003c\/p\u003e \u003cp\u003eInternational and Transatlantic Legislation 219\u003c\/p\u003e \u003cp\u003eRegulating the Tech Sector 221\u003c\/p\u003e \u003cp\u003eSummary 228\u003c\/p\u003e \u003cp\u003eNotes 228\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 By Algorithmic Influencers 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGroup (Re)Think 232\u003c\/p\u003e \u003cp\u003eFlyaway Fairness 238\u003c\/p\u003e \u003cp\u003eAlgorithmic Fairness 239\u003c\/p\u003e \u003cp\u003eBroadening Fairness 241\u003c\/p\u003e \u003cp\u003eModeration Modes 245\u003c\/p\u003e \u003cp\u003eDouble Standards 246\u003c\/p\u003e \u003cp\u003eCalling Out Algorithmic Misogynoir 252\u003c\/p\u003e \u003cp\u003eData and Oversight 254\u003c\/p\u003e \u003cp\u003eSummary 256\u003c\/p\u003e \u003cp\u003eNotes 256\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 By the Public 263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFreeing the Underestimated 264\u003c\/p\u003e \u003cp\u003eLearning Data Civics 267\u003c\/p\u003e \u003cp\u003eThe State of the Data Industry 271\u003c\/p\u003e \u003cp\u003eLiving in the 21st Century 273\u003c\/p\u003e \u003cp\u003eCondemning the Original Stain 277\u003c\/p\u003e \u003cp\u003eTech Safety in Numbers 279\u003c\/p\u003e \u003cp\u003eSummary 283\u003c\/p\u003e \u003cp\u003eNotes 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Code for \u003ci\u003eapp.py \u003c\/i\u003e287\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA 287\u003c\/p\u003e \u003cp\u003eB 288\u003c\/p\u003e \u003cp\u003eC 288\u003c\/p\u003e \u003cp\u003eD 289\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Code for \u003ci\u003escreen.py \u003c\/i\u003e291\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA 291\u003c\/p\u003e \u003cp\u003eB 294\u003c\/p\u003e \u003cp\u003eC 295\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C Code for \u003ci\u003esearch.py \u003c\/i\u003e297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA 297\u003c\/p\u003e \u003cp\u003eB 300\u003c\/p\u003e \u003cp\u003eC 301\u003c\/p\u003e \u003cp\u003eD 303\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix D Pseudocode for \u003ci\u003efaceit.py \u003c\/i\u003e305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix E The Data Visualisation Catalogue’s Visualization Types 309\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix F Glossary 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIndex 315\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407162876247,"sku":"9781119821182","price":24.79,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119821182.jpg?v=1730498390","url":"https:\/\/bookcurl.com\/products\/data-conscience-9781119821182","provider":"Book Curl","version":"1.0","type":"link"}