TY  - BOOK
AU  - Franks,Bill
TI  - 97 things about ethics everyone in data science should know: collective wisdom from the experts 
SN  - 9781492072669
AV  - QA76.9.D343 .F73 2020
U1  - 006.3/12 23
PY  - 2020///.
CY  - Sebastopol, California: 
PB  - O'Reilly
KW  - Data mining
KW  - Social aspects
KW  - Ethics
KW  - Artificial intelligence
KW  - Machine learning
KW  - fast
N1  - Includes bibliographical references and index; 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
N2  - Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today--
ER  -