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035			_a(OCoLC)on1191819373
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050	0	0	_aQA76.9.D343 _bA23 2020
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245	0	0	_a97 things about ethics everyone in data science should know : _bcollective wisdom from the experts / _c[edited by] Bill Franks.
246	3	0	_aNinety-seven things about ethics everyone in data science should know
250			_aFirst edition.
264		1	_aBeijing : _bO'Reilly, _c[2020]
264		4	_c©2020
300			_axix, 323 pages : _billustrations ; _c23 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
505	2	0	_gPart 1. _tFoundational ethical principles. _g1. _tThe truth about AI bias / _rCassie Kozyrkov -- _g2. _tIntroducing ethicize, the fully AI-driven cloud-based ethics solution! / _rBrian T. O'Neill -- _g3. _t"Ethical" is not a binary concept / _rTim Wilson -- _g4. _tCautionary ethics tales : phrenology, eugenics, ... and data science? / _rSherrill Hayes -- _g5. _tLeadership for the future : how to approach ethical transparency / _rRado Kotorov -- _g6. _tRules and rationality / _rChristof Wolf Brenner -- _g7. Understanding passive versus proactive ethics / _rBill Schmarzo -- _g8. _tBe careful with "decisions of the heart" / _rHugh Watson -- _g9. _tFairness in the age of algorithms -- _g10. _tData science ethics : what is the foundational standard? / _rMario Vela -- _g11. _tUnderstand who your leaders serve / _rHassen Masum -- _gPart 2. _tData science and society. _g12. _tUnbiased [is not] fair : for data science, it cannot be just about the math / _rDoug Hague -- _g13. _tTrust, data science, and Stephen Covey / _rJames Taylor -- _g14. _tEthics must be a cornerstone of the data science curriculum / _rLinda Burtch -- _g15. _tData storytelling : the tipping point between fact and fiction / _rBrent Dykes -- _g16. _tInformed consent and data literacy education are crucial to ethics / _rSherrill Hayes -- _g17. _tFirst, do no harm / _rEric Schmidt -- _g18. _tWhy research should be reproducible / _rStuart Buck -- _g19. _tBuild multiperspective AI / _rHassan Masum and Sébastien Paquet -- _g20. _tEthics as a competitive advantage / _rDave Mathias -- _g21. _tAlgorithmic bias : are you a bystander or an upstander? / _rJitendra Mudhol and Heidi Livingston Eisips -- _g22. _tData science and deliberative justice : the ethics of the voice of "the other" / _rRobert J. McGrath -- _g23. _tSpam. Are you going to miss it? / _rJohn Thuma -- _g24. _tIs it wrong to be right? / _rMarty Ellingsworth -- _g25. _tWe're not yet ready for a trustmark for technology / _rHannah Kitcher and Laura James -- _gPart 3. _tThe ethics of data. _g26. _tHow to ask for customers' data with transparency and trust / _rRasmus Wegener -- _g27. _tData ethics and the lemming effect / _rBob Gladden -- _g28. _tPerceptions of personal data / _rIrina Raicu -- _g29. _tShould data have rights? / _rJennifer 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.
520			_aWritten 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-- _cSource other than the Library of Congress.
650		0	_aData mining _xSocial aspects.
650		0	_aData mining _xEthics.
650		0	_aArtificial intelligence _xEthics.
650		0	_aMachine learning _xEthics.
650		7	_aData mining _xSocial aspects _2fast _0(OCoLC)fst01983683
650		7	_aEthics _2fast _0(OCoLC)fst00915833
700	1		_aFranks, Bill, _d1968- _eeditor.
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