97 Things About Ethics Everyone in Data Science Should Know

97 Things About Ethics Everyone in Data Science Should Know

by Bill Franks
Released August 2020
Publisher(s): O'Reilly Media, Inc.
ISBN: 9781492072669

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Book description

Most of the high-profile cases of real or perceived unethical activity in data science aren't matters of bad intent. Rather, they occur because the ethics simply aren't thought through well enough. Being ethical takes constant diligence, and in many situations identifying the right choice can be difficult.

In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically. Data science professionals, managers, and tech leaders will gain a better understanding of ethics through powerful, real-world best practices.

Articles include:

  • Ethics Is Not a Binary Concept—Tim Wilson
  • How to Approach Ethical Transparency—Rado Kotorov
  • Unbiased ≠ Fair—Doug Hague
  • Rules and Rationality—Christof Wolf Brenner
  • The Truth About AI Bias—Cassie Kozyrkov
  • Cautionary Ethics Tales—Sherrill Hayes
  • Fairness in the Age of Algorithms—Anna Jacobson
  • The Ethical Data Storyteller—Brent Dykes
  • Introducing Ethicize™, the Fully AI-Driven Cloud-Based Ethics Solution!—Brian O'Neill
  • Be Careful with "Decisions of the Heart"—Hugh Watson
  • Understanding Passive Versus Proactive Ethics—Bill Schmarzo

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Table of contents

  1. Preface
    1. Why Now?
    2. Ethics Are “Fuzzy”
    3. Take Ownership of Ethics!
    4. How the Book Is Organized
    5. O’Reilly Online Learning
    6. How to Contact Us
    7. Acknowledgments
  2. I. Foundational Ethical Principles
  3. 1. The Truth About AI Bias
    1. Cassie Kozyrkov
  4. 2. Introducing Ethicize™, the fully AI-driven cloud-based ethics solution!
    1. Brian T. O’Neill
  5. 3. “Ethical” Is Not a Binary Concept
    1. Tim Wilson
  6. 4. Cautionary Ethics Tales: Phrenology, Eugenics,​...and Data Science?
    1. Sherrill Hayes
  7. 5. Leadership for the Future: How to Approach Ethical Transparency
    1. Rado Kotorov
  8. 6. Rules and Rationality
    1. Christof Wolf Brenner
  9. 7. Understanding Passive Versus Proactive Ethics
    1. Bill Schmarzo
  10. 8. Be Careful with “Decisions of the Heart”
    1. Hugh Watson
  11. 9. Fairness in the Age of Algorithms
    1. Anna Jacobson
  12. 10. Data Science Ethics: What Is the Foundational Standard?
    1. Mario Vela
  13. 11. Understand Who Your Leaders Serve
    1. Hassan Masum
  14. II. Data Science and Society
  15. 12. Unbiased ≠ Fair: For Data Science, It Cannot Be Just About the Math
    1. Doug Hague
  16. 13. Trust, Data Science, and Stephen Covey
    1. James Taylor
  17. 14. Ethics Must Be a Cornerstone of the Data Science Curriculum
    1. Linda Burtch
  18. 15. Data Storytelling: The Tipping Point Between Fact and Fiction
    1. Brent Dykes
  19. 16. Informed Consent and Data Literacy Education Are Crucial to Ethics
    1. Sherrill Hayes
  20. 17. First, Do No Harm
    1. Eric Schmidt
  21. 18. Why Research Should Be Reproducible
    1. Stuart Buck
  22. 19. Build Multiperspective AI
    1. Hassan Masum and Sébastien Paquet
  23. 20. Ethics as a Competitive Advantage
    1. Dave Mathias
  24. 21. Algorithmic Bias: Are You a Bystander or an Upstander?
    1. Jitendra Mudhol and Heidi Livingston Eisips
  25. 22. Data Science and Deliberative Justice: The Ethics of the Voice of “the Other”
    1. Robert J. McGrath
  26. 23. Spam. Are You Going to Miss It?
    1. John Thuma
  27. 24. Is It Wrong to Be Right?
    1. Marty Ellingsworth
  28. 25. We’re Not Yet Ready for a Trustmark for Technology
    1. Hannah Kitcher and Laura James
  29. III. The Ethics of Data
  30. 26. How to Ask for Customers’ Data with Transparency and Trust
    1. Rasmus Wegener
  31. 27. Data Ethics and the Lemming Effect
    1. Bob Gladden
  32. 28. Perceptions of Personal Data
    1. Irina Raicu
  33. 29. Should Data Have Rights?
    1. Jennifer Lewis Priestley
  34. 30. Anonymizing Data Is Really, Really Hard
    1. Damian Gordon
  35. 31. Just Because You Could, Should You? Ethically Selecting Data for Analytics
    1. Steve Stone
  36. 32. Limit the Viewing of Customer Information by Use Case and Result Sets
    1. Robert J. Abate
  37. 33. Rethinking the “Get the Data” Step
    1. Phil Bangayan
  38. 34. How to Determine What Data Can Be Used Ethically
    1. Leandre Adifon
  39. 35. Ethics Is the Antidote to Data Breaches
    1. Damian Gordon
  40. 36. Ethical Issues Are Front and Center in Today’s Data Landscape
    1. Kenneth Viciana
  41. 37. Silos Create Problems—Perhaps More Than You Think
    1. Bonnie Holub
  42. 38. Securing Your Data Against Breaches Will Help Us Improve Health Care
    1. Fred Nugen
  43. IV. Defining Appropriate Targets & Appropriate Usage
  44. 39. Algorithms Are Used Differently than Human Decision Makers
    1. Rachel Thomas
  45. 40. Pay Off Your Fairness Debt, the Shadow Twin of Technical Debt
    1. Arnobio Morelix
  46. 41. AI Ethics
    1. Cassie Kozyrkov
  47. 42. The Ethical Data Storyteller
    1. Brent Dykes
  48. 43. Imbalance of Factors Affecting Societal Use of Data Science
    1. Nenad Jukić
  49. 44. Probability—the Law That Governs Analytical Ethics
    1. Thomas Casey
  50. 45. Don’t Generalize Until Your Model Does
    1. Michael Hind
  51. 46. Toward Value-Based Machine Learning
    1. Ron Bodkin
  52. 47. The Importance of Building Knowledge in Democratized Data Science Realms
    1. Justin Cochran
  53. 48. The Ethics of Communicating Machine Learning Predictions
    1. Rado Kotorov
  54. 49. Avoid the Wrong Part of the Creepiness Scale
    1. Hugh Watson
  55. 50. Triage and Artificial Intelligence
    1. Peter Bruce
  56. 51. Algorithmic Misclassification—the (Pretty) Good, the Bad, and the Ugly
    1. Arnobio Morelix
  57. 52. The Golden Rule of Data Science
    1. Kris Hunt
  58. 53. Causality and Fairness—Awareness in Machine Learning
    1. Scott Radcliffe
  59. 54. Facial Recognition on the Street and in Shopping Malls
    1. Brendan Tierney
  60. V. Ensuring Proper Transparency & Monitoring
  61. 55. Responsible Design and Use of AI: Managing Safety, Risk, and Transparency
    1. Pamela Passman
  62. 56. Blatantly Discriminatory Algorithms
    1. Eric Siegel
  63. 57. Ethics and Figs: Why Data Scientists Cannot Take Shortcuts
    1. Jennifer Lewis Priestley
  64. 58. What Decisions Are You Making?
    1. James Taylor
  65. 59. Ethics, Trading, and Artificial Intelligence
    1. John Power
  66. 60. The Before, Now, and After of Ethical Systems
    1. Evan Stubbs
  67. 61. Business Realities Will Defeat Your Analytics
    1. Richard Hackathorn
  68. 62. How Can I Know You’re Right?
    1. Majken Sander
  69. 63. A Framework for Managing Ethics in Data Science: Model Risk Management
    1. Doug Hague
  70. 64. The Ethical Dilemma of Model Interpretability
    1. Grant Fleming
  71. 65. Use Model-Agnostic Explanations for Finding Bias in Black-Box Models
    1. Yiannis Kanellopoulos and Andreas Messalas
  72. 66. Automatically Checking for Ethics Violations
    1. Jesse Anderson
  73. 67. Should Chatbots Be Held to a Higher Ethical Standard than Humans?
    1. Naomi Arcadia Kaduwela
  74. 68. “All Models Are Wrong.” What Do We Do About It?
    1. Miroslava Walekova
  75. 69. Data Transparency: What You Don’t Know Can Hurt You
    1. Janella Thomas
  76. 70. Toward Algorithmic Humility
    1. Marc Faddoul
  77. VI. Policy Guidelines
  78. 71. Equally Distributing Ethical Outcomes in a Digital Age
    1. Keyur Desai
  79. 72. Data Ethics—Three Key Actions for the Analytics Leader
    1. John F. Carter
  80. 73. Ethics: The Next Big Wave for Data Science Careers?
    1. Linda Burtch
  81. 74. Framework for Designing Ethics into Enterprise Data
    1. Keri McConnell
  82. 75. Data Science Does Not Need a Code of Ethics
    1. Dave Cherry
  83. 76. How to Innovate Responsibly
    1. Carole Piovesan
  84. 77. Implementing AI Ethics Governance and Control
    1. Steve Stone
  85. 78. Artificial Intelligence: Legal Liabilities amid Emerging Ethics
    1. Pamela Passman
  86. 79. Make Accountability a Priority
    1. Yiannis Kanellopoulos
  87. 80. Ethical Data Science: Both Art and Science
    1. Polly Mitchell-Guthrie
  88. 81. Algorithmic Impact Assessments
    1. Randy Guse
  89. 82. Ethics and Reflection at the Core of Successful Data Science
    1. Mike McGuirk
  90. 83. Using Social Feedback Loops to Navigate Ethical Questions
    1. Nick Hamlin
  91. 84. Ethical CRISP-DM: A Framework for Ethical Data Science Development
    1. Collin Cunningham
  92. 85. Ethics Rules in Applied Econometrics and Data Science
    1. Steven C. Myers
  93. 86. Are Ethics Nothing More than Constraints and Guidelines for Proper Societal Behavior?
    1. Bill Schmarzo
  94. 87. Five Core Virtues for Data Science and Artificial Intelligence
    1. Aaron Burciaga
  95. VII. Case Studies
  96. 88. Auto Insurance: When Data Science and the Business Model Intersect
    1. Edward Vandenberg
  97. 89. To Fight Bias in Predictive Policing, Justice Can’t Be Color-Blind
    1. Eric Siegel
  98. 90. When to Say No to Data
    1. Robert J. Abate
  99. 91. The Paradox of an Ethical Paradox
    1. Bob Gladden
  100. 92. Foundation for the Inevitable Laws for LAWS
    1. Stephanie Seward
  101. 93. A Lifetime Marketing Analyst’s Perspective on Consumer Data Privacy
    1. Mike McGuirk
  102. 94. 100% Conversion: Utopia or Dystopia?
    1. Dave Cherry
  103. 95. Random Selection at Harvard?
    1. Peter Bruce
  104. 96. To Prepare or Not to Prepare for the Storm
    1. Kris Hunt
  105. 97. Ethics, AI, and the Audit Function in Financial Reporting
    1. Steven Mintz
  106. 98. The Gray Line
    1. Phil Broadbent
  107. Contributors
  108. Index
  109. About the Editor
  110. Bill Franks

Product information

  • Title: 97 Things About Ethics Everyone in Data Science Should Know
  • Author(s): Bill Franks
  • Release date: August 2020
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781492072669