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 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
Table of contents
- I. Foundational Ethical Principles
- 1. The Truth About AI Bias
- 2. Introducing Ethicize™, the fully AI-driven cloud-based ethics solution!
- 3. “Ethical” 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
- 7. Understanding Passive Versus Proactive Ethics
- 8. Be Careful with “Decisions of the Heart”
- 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
- 20. Ethics as a Competitive Advantage
- 21. Algorithmic Bias: Are You a Bystander or an Upstander?
- 22. Data Science and Deliberative Justice: The Ethics of the Voice of “the Other”
- 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
- 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?
- 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
- 33. Rethinking the “Get the Data” 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
- 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
- 66. Automatically Checking for Ethics Violations
- 67. Should Chatbots Be Held to a Higher Ethical Standard than Humans?
- 68. “All Models Are Wrong.” 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
- 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
- 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
- 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
- About the Editor
- Bill Franks
- Title: 97 Things About Ethics Everyone in Data Science Should Know
- Release date: August 2020
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492072669
You might also like
Ethics and Data Science
As the impact of data science continues to grow on society there is an increased need …
Ethics of Big Data
This book contains a framework for productive discussion and thinking about ethics and Big Data in …
Data Science for Business
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces …
The Data Analysis Workshop
Learn how to analyze data using Python models with the help of real-world use cases and …