Data Conscience

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##SINGLE_LINE##* EXPLORATIONS OF COMPUTATIONAL THINKING IN PRACTICE##SINGLE_LINE##* STRATEGIES FOR ENCOURAGING ACCOUNTABILITY IN TECH##SINGLE_LINE##* WAYS TO AVOID DOUBLE-EDGED DATA VISUALIZATION##SINGLE_LINE##* SCHEMES FOR GOVERNING DATA STRUCTURES WITH LAW AND ALGORITHMS##SINGLE_LINE##


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.

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