97 things about ethics everyone in data science should know : collective wisdom from the experts / edited by Bill Franks.

Format:

Book

Contributor:

Franks, Bill, 1968- editor.

Language:

English

Subjects (All):

Artificial intelligence--Moral and ethical aspects.

Artificial intelligence.

Computer science--Moral and ethical aspects.

Computer science.

Electronic data processing--Moral and ethical aspects.

Electronic data processing.

Physical Description:

1 online resource (344 pages)

Edition:

First edition.

Other Title:

Ninety-seven things about ethics everyone in data science should know

Collective wisdom from the experts

Place of Publication:

North Sebastopol, California : O'Reilly Media, Inc., [2020]

System Details:

text file

Summary:

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 W ilson 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

Contents:

Part 1. Foundational ethical principles. 1. The truth about AI bias / Cassie Kozyrkov

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

5. Leadership for the future : how to approach ethical transparency / Rado Kotorov

6. Rules and rationality / Christof Wolf Brenner

7. Understanding passive versus proactive ethics / Bill Schmarzo

8. Be careful with "decisions of the heart" / Hugh Watson

9. Fairness in the age of algorithms

10. Data science ethics : what is the foundational standard? / Mario Vela

11. Understand who your leaders serve / Hassen Masum

Part 2. Data science and society. 12. Unbiased [is not] fair : for data science, it cannot be just about the math / Doug Hague

13. Trust, data science, and Stephen Covey / James Taylor

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

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

Part 3. 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

Part III. The ethics of data. Chapter 26. How to ask for customers' data with transparency and trust

Chapter 27. Data ethics and the lemming effect

Chapter 28. Perceptions of personal data

Chapter 29. Should data have rights?

Chapter 30. Anonymizing data is really, really hard

Chapter 31. Just because you could, should you? Ethically selecting data for analytics

Chapter 32. Limit the viewing of customer information by use case and result sets

Chapter 33. Rethinking the "get the data" step

Chapter 34. How to determine what data can be used ethically

Chapter 35. Ethics is the antidote to data breaches

Chapter 36. Ethical issues are front and center in today's data landscape

Chapter 37. Silos create problems, perhaps more than you think

Chapter 38. Securing your data against breaches will help us improve health care

Part IV. Defining appropriate targets & appropriate usage. Chapter 39. Algorithms are used differently than human decision makers

Chapter 40. Pay off your fairness debt, the shadow twin of technical debt

Chapter 41. AI ethics

Chapter 42. The ethical data storyteller

Chapter 43. Imbalance of factors affecting societal use of data science

Chapter 44. Probability

the law that governs analytical ethics

Chapter 45. Don't generalize until your model does

Chapter 46. Toward value-based machine learning

Chapter 47. The importance of building knowledge in democratized data science realms

Chapter 48. The ethics of communicating machine learning predictions

Chapter 49. Avoid the wrong part of the creepiness scale

Chapter 50. Triage and artificial intelligence

Chapter 51. Algorithmic misclassification: the (pretty) good, the bad, and the ugly

Chapter 52. The golden rule of data science

Chapter 53. Causality and fairness

awareness in machine learning

Chapter 54. Facial recognition on the street and in shopping malls

Part V. Ensuring proper transparency & monitoring. Chapter 55. Responsible design and use of AI: managing safety, risk, and transparency

Chapter 56. Blatantly discriminatory algorithms

Chapter 57. Ethics and figs: why data scientists cannot take shortcuts

Chapter 58. What decisions are you making?

Chapter 59. Ethics, trading, and artificial intelligence

Chapter 60. The before, now, and after of ethical systems

Chapter 61. Business realities will defeat your analytics

Chapter 62. How can I know you're right?

Chapter 63. A framework for managing ethics in data science: model risk management

Chapter 64. The ethical dilemma of model interpretability

Chapter 65. Use model-agnostic explanations for finding bias in black-box models

Chapter 66. Automatically checking for ethics violations

Chapter 67. Should chatbots be held to a higher ethical standard than humans?

Chapter 68. "All models are wrong." What do we do about it?

Chapter 69. Data transparency: what you don't know can hurt you

Chapter 70. Toward algorithmic humility

Part VI. Policy guidelines. Chapter 71. Equally distributing ethical outcomes in a digital age

Chapter 72. Data ethics

three key actions for the analytics leader

Chapter 73. Ethics: the next big wave for data science careers?

Chapter 74. Framework for designing ethics into enterprise data

Chapter 75. Data science does not need a code of ethics

Chapter 76. How to innovate responsibly

Chapter 77. Implementing AI ethics governance and control

Chapter 78. Artificial intelligence: legal liabilities amid emerging ethics

Chapter 79. Make accountability a priority

Chapter 80. Ethical data science: both art and science

Chapter 81. Algorithmic impact assessments

Chapter 82. Ethics and reflection at the core of successful data science

Chapter 83. Using social feedback loops to navigate ethical questions

Chapter 84. Ethical CRISP-DM: a framework for ethical data science development

Chapter 85. Ethics rules in applied econometrics and data science

Chapter 86. Are ethics nothing more than constraints and guidelines for proper societal behavior?

Chapter 87. Five core virtues for data science and artificial intelligence

Part VII. Case studies

Chapter 88. Auto insurance: when data science and the business model intersect

Chapter 89. To fight bias in predictive policing, justice can't be color-blind

Chapter 90. When to say no to data

Chapter 91. The paradox of an ethical paradox

Chapter 92. Foundation for the inevitable laws for LAWS

Chapter 93. A lifetime marketing analyst's perspective on consumer data privacy

Chapter 94. 100% conversion: utopia or dystopia?

Chapter 95. Random selection at Harvard?

Chapter 96. To prepare or not to prepare for the storm

Chapter 97. Ethics, AI, and the audit function in financial reporting

Chapter 98. The gray line

Contributors

Index.

Notes:

Online resource; Title from title page (viewed August 7, 2020)

Includes bibliographical references and index.

ISBN:

9781492072652

1492072656

OCLC:

1203113683