MARC details
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09042cam a2200421 i 4500 |
| 001 - CONTROL NUMBER |
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22491926 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
ZET-ke |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20250128090014.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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220404t20202020cc a b 001 0 eng d |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
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| LC control number |
2021278705 |
| 015 ## - NATIONAL BIBLIOGRAPHY NUMBER |
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| National bibliography number |
GBC0D0868 |
| Source |
bnb |
| 016 7# - NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER |
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| Record control number |
019907756 |
| Source |
Uk |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781492072669 |
| Qualifying information |
(paperback) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
1492072664 |
| Qualifying information |
(paperback) |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(OCoLC)on1191819373 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Transcribing agency |
DLC |
| Description conventions |
rda |
| Modifying agency |
ZET-ke |
| 042 ## - AUTHENTICATION CODE |
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| Authentication code |
lccopycat |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA76.9.D343 |
| Item number |
.F73 2020 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.3/12 |
| Edition number |
23 |
| 245 00 - TITLE STATEMENT |
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| Title |
97 things about ethics everyone in data science should know : |
| Remainder of title |
collective wisdom from the experts / |
| Statement of responsibility, etc |
[edited by] Bill Franks. |
| 246 30 - VARYING FORM OF TITLE |
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| Title proper/short title |
Ninety-seven things about ethics everyone in data science should know |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
First edition. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Sebastopol, California: |
| Name of publisher, distributor, etc |
O'Reilly, |
| Date of publication, distribution, etc |
2020. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xix, 323 p.: |
| Other physical details |
ill. ; |
| Dimensions |
23 cm |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 505 20 - FORMATTED CONTENTS NOTE |
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| Miscellaneous information |
Part 1. |
| Title |
Foundational ethical principles. |
| Miscellaneous information |
1. |
| Title |
The truth about AI bias / |
| Statement of responsibility |
Cassie Kozyrkov -- |
| Miscellaneous information |
2. |
| Title |
Introducing ethicize, the fully AI-driven cloud-based ethics solution! / |
| Statement of responsibility |
Brian T. O'Neill -- |
| Miscellaneous information |
3. |
| Title |
"Ethical" is not a binary concept / |
| Statement of responsibility |
Tim Wilson -- |
| Miscellaneous information |
4. |
| Title |
Cautionary ethics tales : phrenology, eugenics, ... and data science? / |
| Statement of responsibility |
Sherrill Hayes -- |
| Miscellaneous information |
5. |
| Title |
Leadership for the future : how to approach ethical transparency / |
| Statement of responsibility |
Rado Kotorov -- |
| Miscellaneous information |
6. |
| Title |
Rules and rationality / |
| Statement of responsibility |
Christof Wolf Brenner -- |
| Miscellaneous information |
7. Understanding passive versus proactive ethics / |
| Statement of responsibility |
Bill Schmarzo -- |
| Miscellaneous information |
8. |
| Title |
Be careful with "decisions of the heart" / |
| Statement of responsibility |
Hugh Watson -- |
| Miscellaneous information |
9. |
| Title |
Fairness in the age of algorithms -- |
| Miscellaneous information |
10. |
| Title |
Data science ethics : what is the foundational standard? / |
| Statement of responsibility |
Mario Vela -- |
| Miscellaneous information |
11. |
| Title |
Understand who your leaders serve / |
| Statement of responsibility |
Hassen Masum -- |
| Miscellaneous information |
Part 2. |
| Title |
Data science and society. |
| Miscellaneous information |
12. |
| Title |
Unbiased [is not] fair : for data science, it cannot be just about the math / |
| Statement of responsibility |
Doug Hague -- |
| Miscellaneous information |
13. |
| Title |
Trust, data science, and Stephen Covey / |
| Statement of responsibility |
James Taylor -- |
| Miscellaneous information |
14. |
| Title |
Ethics must be a cornerstone of the data science curriculum / |
| Statement of responsibility |
Linda Burtch -- |
| Miscellaneous information |
15. |
| Title |
Data storytelling : the tipping point between fact and fiction / |
| Statement of responsibility |
Brent Dykes -- |
| Miscellaneous information |
16. |
| Title |
Informed consent and data literacy education are crucial to ethics / |
| Statement of responsibility |
Sherrill Hayes -- |
| Miscellaneous information |
17. |
| Title |
First, do no harm / |
| Statement of responsibility |
Eric Schmidt -- |
| Miscellaneous information |
18. |
| Title |
Why research should be reproducible / |
| Statement of responsibility |
Stuart Buck -- |
| Miscellaneous information |
19. |
| Title |
Build multiperspective AI / |
| Statement of responsibility |
Hassan Masum and Sébastien Paquet -- |
| Miscellaneous information |
20. |
| Title |
Ethics as a competitive advantage / |
| Statement of responsibility |
Dave Mathias -- |
| Miscellaneous information |
21. |
| Title |
Algorithmic bias : are you a bystander or an upstander? / |
| Statement of responsibility |
Jitendra Mudhol and Heidi Livingston Eisips -- |
| Miscellaneous information |
22. |
| Title |
Data science and deliberative justice : the ethics of the voice of "the other" / |
| Statement of responsibility |
Robert J. McGrath -- |
| Miscellaneous information |
23. |
| Title |
Spam. Are you going to miss it? / |
| Statement of responsibility |
John Thuma -- |
| Miscellaneous information |
24. |
| Title |
Is it wrong to be right? / |
| Statement of responsibility |
Marty Ellingsworth -- |
| Miscellaneous information |
25. |
| Title |
We're not yet ready for a trustmark for technology / |
| Statement of responsibility |
Hannah Kitcher and Laura James -- |
| Miscellaneous information |
Part 3. |
| Title |
The ethics of data. |
| Miscellaneous information |
26. |
| Title |
How to ask for customers' data with transparency and trust / |
| Statement of responsibility |
Rasmus Wegener -- |
| Miscellaneous information |
27. |
| Title |
Data ethics and the lemming effect / |
| Statement of responsibility |
Bob Gladden -- |
| Miscellaneous information |
28. |
| Title |
Perceptions of personal data / |
| Statement of responsibility |
Irina Raicu -- |
| Miscellaneous information |
29. |
| Title |
Should data have rights? / |
| Statement of responsibility |
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. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
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-- |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining |
| General subdivision |
Social aspects. |
| 9 (RLIN) |
27537 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining |
| General subdivision |
Ethics. |
| 9 (RLIN) |
27538 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Artificial intelligence |
| General subdivision |
Ethics. |
| 9 (RLIN) |
27539 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
| General subdivision |
Ethics. |
| 9 (RLIN) |
27540 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining |
| General subdivision |
Social aspects |
| Source of heading or term |
fast |
| 9 (RLIN) |
27537 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Ethics |
| Source of heading or term |
fast |
| 9 (RLIN) |
938 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Franks, Bill, |
| Dates associated with a name |
1968- |
| Relator term |
editor. |
| 9 (RLIN) |
27541 |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
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| a |
7 |
| b |
cbc |
| c |
copycat |
| d |
2 |
| e |
ncip |
| f |
20 |
| g |
y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Books |
| Classification part |
QA76.9.D343 |
| Call number prefix |
QA76.9.D343 |
| Call number suffix |
.F73 |
