Data Conscience: Algorithmic Siege on our Humanity

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 transparency Explorations of computational thinking in practice Strategies for encouraging accountability in tech Ways to avoid double-edged data visualization Schemes for governing data structures with law and algorithms

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

About the Author :
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.