Data Conscience: Algorithmic Siege on our Humanity

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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

    Author(s): Brandeis Hill Marshall
    Publisher: Wiley
    Year: 2022

    Language: English
    Pages: 354
    City: Hoboken

    Cover
    Title Page
    Copyright Page
    About the Author
    About the Technical Editor
    Acknowledgments
    Contents
    Foreword
    Introduction
    Part I Transparency
    Chapter 1 Oppression By. . .
    The Law
    The Science
    Summary
    Notes
    Recommended Reading
    Chapter 2 Morality
    Data Is All Around Us
    Morality and Technology
    Misconceptions of Data Ethics
    Limits of Tech and Data Ethics
    Summary
    Notes
    Chapter 3 Bias
    Types of Bias
    Before You Code
    Bias Messaging
    Summary
    Notes
    Chapter 4 Computational Thinking in Practice
    Ready to Code
    Algorithmic Justice Practice
    Code Cloning
    Summary
    Notes
    Part II Accountability
    Chapter 5 Messy Gathering Grove
    Ask the Why Question
    Collection
    Reformat
    Summary
    Notes
    Chapter 6 Inconsistent Storage Sanctuary
    Ask the “What” Question
    Files, Sheets, and the Cloud
    Modeling Content Associations
    Manipulating with SQL
    Summary
    Notes
    Chapter 7 Circus of Misguided Analysis
    Ask the “How” Question
    Misevaluating the “Cleaned” Dataset
    Overautomating k, K, and Thresholds
    Not Estimating Algorithmic Risk at Scale
    Summary
    Notes
    Chapter 8 Double-Edged Visualization Sword
    Ask the “When” Question
    Critiquing Visual Construction
    Pretty Picture Mirage
    Summary
    Notes
    Part III Governance
    Chapter 9 By the Law
    Federal and State Legislation
    International and Transatlantic Legislation
    Regulating the Tech Sector
    Summary
    Notes
    Chapter 10 By Algorithmic Influencers
    Group (Re)Think
    Flyaway Fairness
    Moderation Modes
    Summary
    Notes
    Chapter 11 By the Public
    Freeing the Underestimated
    Learning Data Civics
    Condemning the Original Stain
    Tech Safety in Numbers
    Summary
    Notes
    Appendix A Code for app.py
    A
    B
    C
    D
    Appendix B Code for screen.py
    A
    B
    C
    Appendix C Code for search.py
    A
    B
    C
    D
    Appendix D Pseudocode for faceit.py
    Appendix E The Data Visualisation Catalogue’s Visualization Types
    Appendix F Glossary
    Index
    EULA