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.
