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

List 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

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.

Summary

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

Product details

• Authors: Timnit Gebru, B Marshall, Brandeis Marshall, Brandeis Hill Marshall, Brandeis Hill (Rensselaer Polytechnic In Marshall
• Assisted by: Timnit Gebru (Foreword)
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Paperback / Softback
• Released: 30.09.2022
• EAN: 9781119821182
• ISBN: 978-1-119-82118-2
• No. of pages: 352
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > Data communication, networks; Statistik, Informatik, Datenanalyse, Datenbanken, Statistics, computer science, data analysis, Database & Data Warehousing Technologies, Datenbanken u. Data Warehousing