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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.
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 EthicizeT, 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
Table of contents
Ethics Are "Fuzzyö
Take Ownership of Ethics!
How the Book Is Organized
O'Reilly Online Learning
How to Contact Us
I. Foundational Ethical Principles
1. The Truth About AI Bias
2. Introducing EthicizeT, the fully AI-driven cloud-based ethics solution!
Brian T. O'Neill
3. "Ethicalö Is Not a Binary Concept
4. Cautionary Ethics Tales: Phrenology, Eugenics,?...and Data Science?
5. Leadership for the Future: How to Approach Ethical Transparency
6. Rules and Rationality
Christof Wolf Brenner
7. Understanding Passive Versus Proactive Ethics
8. Be Careful with "Decisions of the Heartö
9. Fairness in the Age of Algorithms
10. Data Science Ethics: What Is the Foundational Standard?
11. Understand Who Your Leaders Serve
II. Data Science and Society
12. Unbiased ? Fair: For Data Science, It Cannot Be Just About the Math
13. Trust, Data Science, and Stephen Covey
14. Ethics Must Be a Cornerstone of the Data Science Curriculum
15. Data Storytelling: The Tipping Point Between Fact and Fiction
16. Informed Consent and Data Literacy Education Are Crucial to Ethics
17. First, Do No Harm
18. Why Research Should Be Reproducible
19. Build Multiperspective AI
Hassan Masum and Sébastien Paquet
20. Ethics as a Competitive Advantage
21. Algorithmic Bias: Are You a Bystander or an Upstander?
Jitendra Mudhol and Heidi Livingston Eisips
22. Data Science and Deliberative Justice: The Ethics of the Voice of "the Otherö
Robert J. McGrath
23. Spam. Are You Going to Miss It?
24. Is It Wrong to Be Right?
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
27. Data Ethics and the Lemming Effect
28. Perceptions of Personal Data
29. Should Data Have Rights?
Jennifer Lewis Priestley
30. Anonymizing Data Is Really, Really Hard
31. Just Because You Could, Should You? Ethically Selecting Data for Analytics
32. Limit the Viewing of Customer Information by Use Case and Result Sets
Robert J. Abate
33. Rethinking the "Get the Dataö Step
34. How to Determine What Data Can Be Used Ethically
35. Ethics Is the Antidote to Data Breaches
36. Ethical Issues Are Front and Center in Today's Data Landscape
37. Silos Create Problems-Perhaps More Than You Think
38. Securing Your Data Against Breaches Will Help Us Improve Health Care
IV. Defining Appropriate Targets & Appropriate Usage
39. Algorithms Are Used Differently than Human Decision Makers
40. Pay Off Your Fairness Debt, the Shadow Twin of Technical Debt
41. AI Ethics
42. The Ethical Data Storyteller
43. Imbalance of Factors Affecting Societal Use of Data Science
44. Probability-the Law That Governs Analytical Ethics
45. Don't Generalize Until Your Model Does
46. Toward Value-Based Machine Learning
47. The Importance of Building Knowledge in Democratized Data Science Realms
48. The Ethics of Communicating Machine Learning Predictions
49. Avoid the Wrong Part of the Creepiness Scale
50. Triage and Artificial Intelligence
51. Algorithmic Misclassification-the (Pretty) Good, the Bad, and the Ugly
52. The Golden Rule of Data Science
53. Causality and Fairness-Awareness in Machine Learning
54. Facial Recognition on the Street and in Shopping Malls
V. Ensuring Proper Transparency & Monitoring
55. Responsible Design and Use of AI: Managing Safety, Risk, and Transparency
56. Blatantly Discriminatory Algorithms
57. Ethics and Figs: Why Data Scientists Cannot Take Shortcuts
Jennifer Lewis Priestley
58. What Decisions Are You Making?
59. Ethics, Trading, and Artificial Intelligence
60. The Before, Now, and After of Ethical Systems
61. Business Realities Will Defeat Your Analytics
62. How Can I Know You're Right?
63. A Framework for Managing Ethics in Data Science: Model Risk Management
64. The Ethical Dilemma of Model Interpretability
65. Use Model-Agnostic Explanations for Finding Bias in Black-Box Models
Yiannis Kanellopoulos and Andreas Messalas
66. Automatically Checking for Ethics Violations
67. Should Chatbots Be Held to a Higher Ethical Standard than Humans?
Naomi Arcadia Kaduwela
68. "All Models Are Wrong.ö What Do We Do About It?
69. Data Transparency: What You Don't Know Can Hurt You
70. Toward Algorithmic Humility
VI. Policy Guidelines
71. Equally Distributing Ethical Outcomes in a Digital Age
72. Data Ethics-Three Key Actions for the Analytics Leader
John F. Carter
73. Ethics: The Next Big Wave for Data Science Careers?
74. Framework for Designing Ethics into Enterprise Data
75. Data Science Does Not Need a Code of Ethics
76. How to Innovate Responsibly
77. Implementing AI Ethics Governance and Control
78. Artificial Intelligence: Legal Liabilities amid Emerging Ethics
79. Make Accountability a Priority
80. Ethical Data Science: Both Art and Science
81. Algorithmic Impact Assessments
82. Ethics and Reflection at the Core of Successful Data Science
83. Using Social Feedback Loops to Navigate Ethical Questions
84. Ethical CRISP-DM: A Framework for Ethical Data Science Development
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?
87. Five Core Virtues for Data Science and Artificial Intelligence
VII. Case Studies
88. Auto Insurance: When Data Science and the Business Model Intersect
89. To Fight Bias in Predictive Policing, Justice Can't Be Color-Blind
90. When to Say No to Data
Robert J. Abate
91. The Paradox of an Ethical Paradox
92. Foundation for the Inevitable Laws for LAWS
93. A Lifetime Marketing Analyst's Perspective on Consumer Data Privacy
94. 100% Conversion: Utopia or Dystopia?
95. Random Selection at Harvard?
96. To Prepare or Not to Prepare for the Storm
97. Ethics, AI, and the Audit Function in Financial Reporting
98. The Gray Line