97 things about ethics everyone in data science should know : collective wisdom from the experts /
Ninety-seven things about ethics everyone in data science should know
[edited by] Bill Franks.
- First edition.
- xix, 323 pages : illustrations ; 23 cm
Includes bibliographical references and index.
Foundational ethical principles. The truth about AI bias / Introducing ethicize, the fully AI-driven cloud-based ethics solution! / "Ethical" is not a binary concept / Cautionary ethics tales : phrenology, eugenics, ... and data science? / Leadership for the future : how to approach ethical transparency / Rules and rationality / Be careful with "decisions of the heart" / Fairness in the age of algorithms -- Data science ethics : what is the foundational standard? / Understand who your leaders serve / Data science and society. Unbiased [is not] fair : for data science, it cannot be just about the math / Trust, data science, and Stephen Covey / Ethics must be a cornerstone of the data science curriculum / Data storytelling : the tipping point between fact and fiction / Informed consent and data literacy education are crucial to ethics / First, do no harm / Why research should be reproducible / Build multiperspective AI / Ethics as a competitive advantage / Algorithmic bias : are you a bystander or an upstander? / Data science and deliberative justice : the ethics of the voice of "the other" / Spam. Are you going to miss it? / Is it wrong to be right? / We're not yet ready for a trustmark for technology / The ethics of data. How to ask for customers' data with transparency and trust / Data ethics and the lemming effect / Perceptions of personal data / Should data have rights? / Cassie Kozyrkov -- Brian T. O'Neill -- Tim Wilson -- Sherrill Hayes -- Rado Kotorov -- Christof Wolf Brenner -- Bill Schmarzo -- Hugh Watson -- Mario Vela -- Hassen Masum -- Doug Hague -- James Taylor -- Linda Burtch -- Brent Dykes -- Sherrill Hayes -- Eric Schmidt -- Stuart Buck -- Hassan Masum and Sébastien Paquet -- Dave Mathias -- Jitendra Mudhol and Heidi Livingston Eisips -- Robert J. McGrath -- John Thuma -- Marty Ellingsworth -- Hannah Kitcher and Laura James -- Rasmus Wegener -- Bob Gladden -- Irina Raicu -- 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. Part 1. 1. 2. 3. 4. 5. 6. 7. Understanding passive versus proactive ethics / 8. 9. 10. 11. Part 2. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. Part 3. 26. 27. 28. 29.
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--
9781492072669 1492072664
2021278705
GBC0D0868 bnb
019907756 Uk
Data mining--Social aspects.
Data mining--Ethics.
Artificial intelligence--Ethics.
Machine learning--Ethics.
Data mining--Social aspects
Ethics
QA76.9.D343 / A23 2020
006.3/12
Includes bibliographical references and index.
Foundational ethical principles. The truth about AI bias / Introducing ethicize, the fully AI-driven cloud-based ethics solution! / "Ethical" is not a binary concept / Cautionary ethics tales : phrenology, eugenics, ... and data science? / Leadership for the future : how to approach ethical transparency / Rules and rationality / Be careful with "decisions of the heart" / Fairness in the age of algorithms -- Data science ethics : what is the foundational standard? / Understand who your leaders serve / Data science and society. Unbiased [is not] fair : for data science, it cannot be just about the math / Trust, data science, and Stephen Covey / Ethics must be a cornerstone of the data science curriculum / Data storytelling : the tipping point between fact and fiction / Informed consent and data literacy education are crucial to ethics / First, do no harm / Why research should be reproducible / Build multiperspective AI / Ethics as a competitive advantage / Algorithmic bias : are you a bystander or an upstander? / Data science and deliberative justice : the ethics of the voice of "the other" / Spam. Are you going to miss it? / Is it wrong to be right? / We're not yet ready for a trustmark for technology / The ethics of data. How to ask for customers' data with transparency and trust / Data ethics and the lemming effect / Perceptions of personal data / Should data have rights? / Cassie Kozyrkov -- Brian T. O'Neill -- Tim Wilson -- Sherrill Hayes -- Rado Kotorov -- Christof Wolf Brenner -- Bill Schmarzo -- Hugh Watson -- Mario Vela -- Hassen Masum -- Doug Hague -- James Taylor -- Linda Burtch -- Brent Dykes -- Sherrill Hayes -- Eric Schmidt -- Stuart Buck -- Hassan Masum and Sébastien Paquet -- Dave Mathias -- Jitendra Mudhol and Heidi Livingston Eisips -- Robert J. McGrath -- John Thuma -- Marty Ellingsworth -- Hannah Kitcher and Laura James -- Rasmus Wegener -- Bob Gladden -- Irina Raicu -- 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. Part 1. 1. 2. 3. 4. 5. 6. 7. Understanding passive versus proactive ethics / 8. 9. 10. 11. Part 2. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. Part 3. 26. 27. 28. 29.
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--
9781492072669 1492072664
2021278705
GBC0D0868 bnb
019907756 Uk
Data mining--Social aspects.
Data mining--Ethics.
Artificial intelligence--Ethics.
Machine learning--Ethics.
Data mining--Social aspects
Ethics
QA76.9.D343 / A23 2020
006.3/12