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050 4 QA76.9.D343|bN56 2020eb
082 04 006.3/12|223
090 QA76.9.D343|bN56 2020eb
245 00 97 things about ethics everyone in data science should
know :|bcollective wisdom from the experts /|c[edited by]
Bill Franks.
246 30 Ninety-seven things about ethics everyone in data science
should know
250 First edition.
264 1 Sebastopol, California :|bO'Reilly,|c2020.
264 4 |c©2020
300 1 online resource (xx, 320 pages) :|billustrations
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
347 text file|2rdaft
504 Includes bibliographical references and index.
505 0 Preface -- Part I. Foundational ethical principles.
Chapter 1. The truth about AI bias -- Chapter 2.
Introducing ethicize, the fully AI-driven cloud-based
ethics solution! -- Chapter 3. "Ethical" is not a binary
concept -- Chapter 4. Cautionary ethics tales: phrenology,
eugenics ... and data science? -- Chapter 5. Leadership
for the future: how to approach ethical transparency --
Chapter 6. Rules and rationality -- Chapter 7.
Understanding passive versus proactive ethics -- Chapter
8. Be Careful with "decisions of the heart" -- Chapter 9.
Fairness in the age of algorithms -- Chapter 10. Data
science ethics: what is the foundational standard? --
Chapter 11. Understand who your leaders serve.
505 8 Part II. Data science and society. Chapter 12. Unbiased ≠
fair: for data science, it cannot be just about the math -
- Chapter 13. Trust, data science, and Stephen Covey --
Chapter 14. Ethics must be a cornerstone of the data
science curriculum -- Chapter 15. Data storytelling: the
tipping point between fact and fiction -- Chapter 16.
Informed consent and data literacy education are crucial
to ethics -- Chapter 17. First, do no harm -- Chapter 18.
Why research should be reproducible -- Chapter 19. Build
multiperspective AI -- Chapter 20. Ethics as a competitive
advantage -- Chapter 21. Algorithmic bias: are you a
bystander or an upstander? -- Chapter 22. Data science and
deliberative justice: the ethics of the voice of "the
other" -- Chapter 23. Spam. Are you going to miss it? --
Chapter 24. Is it wrong to be right? -- Chapter 25. We're
not yet ready for a trustmark for technology.
505 8 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.
505 8 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.
505 8 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.
505 8 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.
505 8 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
506 1 Concurrent user level: 1 user
520 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.
561 Purchased with the Phippen Library Fund.
588 0 Print version record; online resource viewed January 13,
2021.
650 0 Data mining|0https://id.loc.gov/authorities/subjects/
sh97002073|xMoral and ethical aspects.|0https://id.loc.gov
/authorities/subjects/sh00006099
650 0 Data mining|0https://id.loc.gov/authorities/subjects/
sh97002073|xSocial aspects.|0https://id.loc.gov/
authorities/subjects/sh00002758
650 0 Ethics.|0https://id.loc.gov/authorities/subjects/
sh85045096
650 7 Data mining.|2fast|0https://id.worldcat.org/fast/887946
650 7 Data mining|xSocial aspects.|2fast|0https://
id.worldcat.org/fast/1983683
650 7 Ethics.|2fast|0https://id.worldcat.org/fast/915833
655 4 Electronic books.
700 1 Franks, Bill,|d1968-|0https://id.loc.gov/authorities/names
/n2011083549|eauthor.
776 08 |iPrint version:|t97 things about ethics everyone in data
science should know.|bFirst edition.|dSebastopol,
California : O'Reilly, 2020|z9781492072669
|w(OCoLC)1191819373
856 40 |zOnline ebook via EBSCO. Access restricted to current
Rider University students, faculty, and staff.|uhttps://
rider.idm.oclc.org/login?url=https://search.ebscohost.com/
login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&
AN=2565959
856 42 |3Instructions for reading/downloading the EBSCO version
of this ebook|uhttp://guides.rider.edu/ebooks/ebsco
901 MARCIVE 20231220
948 |d20211130|cMH|tebscopurchased|lridw
994 C0|bRID
