Data Conscience

Algorithmic Siege on our Humanity

Häftad, Engelska, 2022

328 kr

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Beskrivning

DATA 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 use—and misuse—lies 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

Produktinformation

Utgivningsdatum:2022-10-17
Mått:180 x 231 x 23 mm
Vikt:431 g
Format:Häftad
Språk:Engelska
Antal sidor:352
Förlag:John Wiley & Sons Inc
Medarbetare:Gebru,Timnit
ISBN:9781119821182

Utforska kategorier

Programmeringsböcker inom Data och IT

Mer om författaren

DR. BRANDEIS HILL MARSHALL, PhD, is a computer scientist, tech educator, and data equity consultant. She is a thought leader in broadening participating in data science and puts inclusivity and equity at the center of her work. She obtained her doctorate in Computer Science from Rensselaer Polytechnic Institute.

Innehållsförteckning

Foreword xixIntroduction xxiPart I Transparency 1Chapter 1 Oppression By. . . 3The Law 4Slave Codes 5Black Codes 5The Rise of Jim Crow Laws 8Breaking Open Jim Crow Laws 11Overt Surveillance 12Surveillance at Scale 13The Science 16Numbers 16Anthropometry 18Eugenics 19Summary 23Notes 23Recommended Reading 25Chapter 2 Morality 27Data Is All Around Us 29Morality and Technology 33Defining Tech Ethics 33Mapping Tech Ethics to Human Ethics 39Squeezing in Data Ethics 45Misconceptions of Data Ethics 49Misconception 1: Goodness of Data, andTech by Proxy, Is Apolitical or Bipartisan 49Misconception 2: Data Ethics Is Focused Solely on Laws Protecting Confidentiality and Privacy 50Misconception 3: Implementing Data Ethics Practices Will Make Data Objective 52Notable Misconception Mentions: Ethics and Diversity, Equity, and Inclusion (DEI) Are Interchangeable 53Another Notable Mention: Software Developers Are Only Responsible for Societal Outcomes Stemming from Their Code 54Limits of Tech and Data Ethics 55Summary 57Notes 57Chapter 3 Bias 61Types of Bias 62Defining Bias 63Concrete Example of Biases 65The Bias Wheel 70Before You Code 73Case Study Scenario: Data Sourcing for an Employee Candidate Résumé Database 77Case Study Scenario: Data Manipulation for an Employee Candidate Résumé Database 78Case Study Scenario: Data Interpretation for an EmployeeCandidate Résumé Database 82Bias Messaging 83Summary 83Notes 84Chapter 4 Computational Thinking in Practice 87Ready to Code 88The Shampoo Algorithm 89Computational Thinking 91Coding Environments 93Algorithmic Justice Practice 95Code Cloning 97Socio-Techno-Ethical Review: app.py 101Socio-Techno-Ethical Review: screen.py 103Socio-Techno-Ethical Review: search.py 109Summary 114Notes 114Part II Accountability 117Chapter 5 Messy Gathering Grove 119Ask the Why Question 120Collection 124Open Source Dataset Example: Deciding Data Ownership 127Open Source Dataset Example: Considering Data Privacy 129Reformat 133Summary 139Notes 139Chapter 6 Inconsistent Storage Sanctuary 143Ask the “What” Question 144Files, Sheets, and the Cloud 146Decisions in a Vacuum 149Case Study: Black Twitter 150Modeling Content Associations 153Manipulating with SQL 158Summary 160Notes 161Chapter 7 Circus of Misguided Analysis 163Ask the “How” Question 164Misevaluating the “Cleaned” Dataset 169Overautomating k, K, and Thresholds 177Deepfake Technology 179Not Estimating Algorithmic Risk at Scale 185Summary 187Notes 187Chapter 8 Double-Edged Visualization Sword 191Ask the “When” Question 192Critiquing Visual Construction 197Disabilities in View 201Pretty Picture Mirage 204Case Study: SAT College Board Dataset 207Summary 208Notes 209Part III Governance 213Chapter 9 By the Law 215Federal and State Legislation 216International and Transatlantic Legislation 219Regulating the Tech Sector 221Summary 228Notes 228Chapter 10 By Algorithmic Influencers 231Group (Re)Think 232Flyaway Fairness 238Algorithmic Fairness 239Broadening Fairness 241Moderation Modes 245Double Standards 246Calling Out Algorithmic Misogynoir 252Data and Oversight 254Summary 256Notes 256Chapter 11 By the Public 263Freeing the Underestimated 264Learning Data Civics 267The State of the Data Industry 271Living in the 21st Century 273Condemning the Original Stain 277Tech Safety in Numbers 279Summary 283Notes 283Appendix A Code for app.py 287A 287B 288C 288D 289Appendix B Code for screen.py 291A 291B 294C 295Appendix C Code for search.py 297A 297B 300C 301D 303Appendix D Pseudocode for faceit.py 305Appendix E The Data Visualisation Catalogue’s Visualization Types 309Appendix F Glossary 313Index 315