97 Things About Ethics Everyone in Data Science Should Know by Bill Franks -- Read -- Imperial Library of Trantor

Index

Preface

Why Now? Ethics Are “Fuzzy” Take Ownership of Ethics! How the Book Is Organized O’Reilly Online Learning How to Contact Us Acknowledgments

I. Foundational Ethical Principles 1. The Truth About AI Bias

Cassie Kozyrkov

Data and Math Don’t Equal Objectivity What Is Algorithmic Bias? Datasets Have Human Authors This Is No Excuse to Be a Jerk Fairness in AI Fair and Aware

2. Introducing Ethicize™, the fully AI-driven cloud-based ethics solution!

Brian T. O’Neill

3. “Ethical” Is Not a Binary Concept

Tim Wilson

4. Cautionary Ethics Tales: Phrenology, Eugenics,...and Data Science?

Sherrill Hayes

So What Did Phrenologists and Eugenicists Do? So What Was the Problem? What About Data Science? Conclusions

5. Leadership for the Future: How to Approach Ethical Transparency

Rado Kotorov

1. Playing God 2. Moral Blinding How Should Companies Tackle Such Issues?

6. Rules and Rationality

Christof Wolf Brenner

7. Understanding Passive Versus Proactive Ethics

Bill Schmarzo

What Is AI Ethics? The Ramifications of Unintended Consequences Defining the AI Utility Function Passive Ethics Versus Proactive Ethics Summary

8. Be Careful with “Decisions of the Heart”

Hugh Watson

9. Fairness in the Age of Algorithms

Anna Jacobson

References

10. Data Science Ethics: What Is the Foundational Standard?

Mario Vela

11. Understand Who Your Leaders Serve

Hassan Masum

II. Data Science and Society 12. Unbiased ≠ Fair: For Data Science, It Cannot Be Just About the Math

Doug Hague

13. Trust, Data Science, and Stephen Covey

James Taylor

Listen First Extend Trust Clarify Expectations Confront Reality Create Transparency Deliver Results Practice Accountability Get Better

14. Ethics Must Be a Cornerstone of the Data Science Curriculum

Linda Burtch

15. Data Storytelling: The Tipping Point Between Fact and Fiction

Brent Dykes

16. Informed Consent and Data Literacy Education Are Crucial to Ethics

Sherrill Hayes

17. First, Do No Harm

Eric Schmidt

18. Why Research Should Be Reproducible

Stuart Buck

19. Build Multiperspective AI

Hassan Masum and Sébastien Paquet

20. Ethics as a Competitive Advantage

Dave Mathias

21. Algorithmic Bias: Are You a Bystander or an Upstander?

Jitendra Mudhol and Heidi Livingston Eisips

Understanding Bystanderism Are You a Bystander or an Upstander? The Time to Be an Upstander Is Now

22. Data Science and Deliberative Justice: The Ethics of the Voice of “the Other”

Robert J. McGrath

23. Spam. Are You Going to Miss It?

John Thuma

24. Is It Wrong to Be Right?

Marty Ellingsworth

25. We’re Not Yet Ready for a Trustmark for Technology

Hannah Kitcher and Laura James

III. The Ethics of Data 26. How to Ask for Customers’ Data with Transparency and Trust

Rasmus Wegener

27. Data Ethics and the Lemming Effect

Bob Gladden

28. Perceptions of Personal Data

Irina Raicu

29. Should Data Have Rights?

Jennifer Lewis Priestley

30. Anonymizing Data Is Really, Really Hard

Damian Gordon

31. Just Because You Could, Should You? Ethically Selecting Data for Analytics

Steve Stone

32. Limit the Viewing of Customer Information by Use Case and Result Sets

Robert J. Abate

33. Rethinking the “Get the Data” Step

Phil Bangayan

34. How to Determine What Data Can Be Used Ethically

Leandre Adifon

35. Ethics Is the Antidote to Data Breaches

Damian Gordon

36. Ethical Issues Are Front and Center in Today’s Data Landscape

Kenneth Viciana

37. Silos Create Problems—Perhaps More Than You Think

Bonnie Holub

38. Securing Your Data Against Breaches Will Help Us Improve Health Care

Fred Nugen

IV. Defining Appropriate Targets & Appropriate Usage 39. Algorithms Are Used Differently than Human Decision Makers

Rachel Thomas

40. Pay Off Your Fairness Debt, the Shadow Twin of Technical Debt

Arnobio Morelix

41. AI Ethics

Cassie Kozyrkov

Levels of Distraction AI Automates the Ineffable AI Enables Thoughtlessness Am I Afraid of AI?

42. The Ethical Data Storyteller

Brent Dykes

43. Imbalance of Factors Affecting Societal Use of Data Science

Nenad Jukić

44. Probability—the Law That Governs Analytical Ethics

Thomas Casey

When Probability and Ethics Collide How Humans Try to Interject Ethics into Algorithms The Ethical Implications of Nonhuman Decision Making

45. Don’t Generalize Until Your Model Does

Michael Hind

46. Toward Value-Based Machine Learning

Ron Bodkin

An Example of the Importance of Values How to Proceed?

47. The Importance of Building Knowledge in Democratized Data Science Realms

Justin Cochran

48. The Ethics of Communicating Machine Learning Predictions

Rado Kotorov

49. Avoid the Wrong Part of the Creepiness Scale

Hugh Watson

50. Triage and Artificial Intelligence

Peter Bruce

The Triage Nurse The Ranking of Records Ethics in Data Science

51. Algorithmic Misclassification—the (Pretty) Good, the Bad, and the Ugly

Arnobio Morelix

52. The Golden Rule of Data Science

Kris Hunt

53. Causality and Fairness—Awareness in Machine Learning

Scott Radcliffe

54. Facial Recognition on the Street and in Shopping Malls

Brendan Tierney

V. Ensuring Proper Transparency & Monitoring 55. Responsible Design and Use of AI: Managing Safety, Risk, and Transparency

Pamela Passman

Security and Safety Ongoing Risk Management Transparency Conclusion

56. Blatantly Discriminatory Algorithms

Eric Siegel

57. Ethics and Figs: Why Data Scientists Cannot Take Shortcuts

Jennifer Lewis Priestley

58. What Decisions Are You Making?

James Taylor

Designing Ethical Decision-Making Systems Demonstrating Ethical Decision Making

59. Ethics, Trading, and Artificial Intelligence

John Power

60. The Before, Now, and After of Ethical Systems

Evan Stubbs

61. Business Realities Will Defeat Your Analytics

Richard Hackathorn

Conceiving Developing Deploying Governing Conclusion

62. How Can I Know You’re Right?

Majken Sander

Data Literacy for Data Users Declare Your Work

63. A Framework for Managing Ethics in Data Science: Model Risk Management

Doug Hague

Data Math Performance Appropriate Use Monitoring Validation Summary

64. The Ethical Dilemma of Model Interpretability

Grant Fleming

65. Use Model-Agnostic Explanations for Finding Bias in Black-Box Models

Yiannis Kanellopoulos and Andreas Messalas

66. Automatically Checking for Ethics Violations

Jesse Anderson

67. Should Chatbots Be Held to a Higher Ethical Standard than Humans?

Naomi Arcadia Kaduwela

Examples of Chatbots Inheriting Human Biases How Chatbots Perpetuate Human Biases Ways to Correct Biases in Chatbots Why Continuous Learning Is Required for Chatbots

68. “All Models Are Wrong.” What Do We Do About It?

Miroslava Walekova

1. Prevent 2. Rectify 3. Improve

69. Data Transparency: What You Don’t Know Can Hurt You

Janella Thomas

70. Toward Algorithmic Humility

Marc Faddoul

VI. Policy Guidelines 71. Equally Distributing Ethical Outcomes in a Digital Age

Keyur Desai

72. Data Ethics—Three Key Actions for the Analytics Leader

John F. Carter

73. Ethics: The Next Big Wave for Data Science Careers?

Linda Burtch

74. Framework for Designing Ethics into Enterprise Data

Keri McConnell

Take a Tiered Approach Do Your Research Identify and Engage Your Stakeholders Be Agile

75. Data Science Does Not Need a Code of Ethics

Dave Cherry

76. How to Innovate Responsibly

Carole Piovesan

77. Implementing AI Ethics Governance and Control

Steve Stone

Adopt an AI Code of Ethical Conduct Stress Diversity in Hiring and Recruiting Ensure Compliance with an Ethical Review Board Establish Audit and Feedback Loops

78. Artificial Intelligence: Legal Liabilities amid Emerging Ethics

Pamela Passman

Data Privacy Cybersecurity Use for Lawful Purposes

79. Make Accountability a Priority

Yiannis Kanellopoulos

80. Ethical Data Science: Both Art and Science

Polly Mitchell-Guthrie

81. Algorithmic Impact Assessments

Randy Guse

82. Ethics and Reflection at the Core of Successful Data Science

Mike McGuirk

83. Using Social Feedback Loops to Navigate Ethical Questions

Nick Hamlin

84. Ethical CRISP-DM: A Framework for Ethical Data Science Development

Collin Cunningham

Business Understanding Data Understanding Data Preparation Modeling Evaluation and Deployment

85. Ethics Rules in Applied Econometrics and Data Science

Steven C. Myers

86. Are Ethics Nothing More than Constraints and Guidelines for Proper Societal Behavior?

Bill Schmarzo

Asimov’s Three Laws of Robotics Ethics Summary

87. Five Core Virtues for Data Science and Artificial Intelligence

Aaron Burciaga

1. Resilience 2. Humility 3. Grit 4. Liberal Education 5. Empathy Conclusion

VII. Case Studies 88. Auto Insurance: When Data Science and the Business Model Intersect

Edward Vandenberg

89. To Fight Bias in Predictive Policing, Justice Can’t Be Color-Blind

Eric Siegel

90. When to Say No to Data

Robert J. Abate

91. The Paradox of an Ethical Paradox

Bob Gladden

92. Foundation for the Inevitable Laws for LAWS

Stephanie Seward

Performance Expectation Methodology (PEM) LAWS Performance During PEM PEM: Continuous and Cyclical Extensions to PEM

93. A Lifetime Marketing Analyst’s Perspective on Consumer Data Privacy

Mike McGuirk

94. 100% Conversion: Utopia or Dystopia?

Dave Cherry

95. Random Selection at Harvard?

Peter Bruce

“An art collection that could conceivably come our way...” Another Way Random Selection with Geographic Stratification

96. To Prepare or Not to Prepare for the Storm

Kris Hunt

97. Ethics, AI, and the Audit Function in Financial Reporting

Steven Mintz

98. The Gray Line

Phil Broadbent

Contributors Index

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