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...

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Weitere Verfasser:	Franks, Bill 1968- (HerausgeberIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Beijing O'Reilly [2020]
Schlagworte:	Data mining > Social aspects Ethics Ethik Data Mining
Online-Zugang:	UBY01
Zusammenfassung:	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. Were 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 Todays Data Landsca++ 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. Probabilitythe Law That Governs Analytical Ethics 45. Dont 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 Misclassificationthe (Pretty) Good, the Bad, and the Ugly 52. The Golden Rule of Data Science 53. Causality and FairnessAwareness 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 Youre 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 Dont Know Can Hurt You 70. Toward Algorithmic Humility --- VI. Policy Guidelines 71. Equally ++ 72. Data EthicsThree 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 Cant 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 Analysts 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
Beschreibung:	Online-Ressource (340 Seiten)
ISBN:	9781492072614 1492072613