97 things about ethics everyone in data science should know : (Record no. 772)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | BD-NoSTU |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220917133219.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220404t20202020cc a b 001 0 eng d |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER | |
| LC control number | 2021278705 |
| 015 ## - NATIONAL BIBLIOGRAPHY NUMBER | |
| National bibliography number | GBC0D0868 |
| Source | bnb |
| 016 7# - NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER | |
| Record control number | 019907756 |
| Source | Uk |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781492072669 |
| Qualifying information | (paperback) |
| International Standard Book Number | 1492072664 |
| Qualifying information | (paperback) |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)on1191819373 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | CGP |
| Language of cataloging | eng |
| Transcribing agency | CGP |
| Description conventions | rda |
| Modifying agency | OCLCO |
| -- | YDX |
| -- | UKMGB |
| -- | OCLCF |
| -- | JRZ |
| -- | A7U |
| -- | OUP |
| -- | BDX |
| -- | GK8 |
| -- | UAP |
| -- | OCLCO |
| -- | NWQ |
| -- | IBI |
| -- | OCLCQ |
| -- | DLC |
| -- | BD-NoSTU |
| 042 ## - AUTHENTICATION CODE | |
| Authentication code | lccopycat |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA76.9.D343 |
| Item number | A23 2020 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3/12 |
| Edition number | 23 |
| 245 00 - TITLE STATEMENT | |
| 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 | |
| Title proper/short title | Ninety-seven things about ethics everyone in data science should know |
| 250 ## - EDITION STATEMENT | |
| Edition statement | First edition. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Beijing : |
| Name of producer, publisher, distributor, manufacturer | O'Reilly, |
| Date of production, publication, distribution, manufacture, or copyright notice | [2020] |
| Date of production, publication, distribution, manufacture, or copyright notice | ©2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xix, 323 pages : |
| Other physical details | illustrations ; |
| Dimensions | 23 cm |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | unmediated |
| Media type code | n |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | volume |
| Carrier type code | nc |
| Source | rdacarrier |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references and index. |
| 505 20 - FORMATTED CONTENTS NOTE | |
| 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-- |
| Assigning source | Source other than the Library of Congress. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data mining |
| General subdivision | Social aspects. |
| Topical term or geographic name as entry element | Data mining |
| General subdivision | Ethics. |
| Topical term or geographic name as entry element | Artificial intelligence |
| General subdivision | Ethics. |
| Topical term or geographic name as entry element | Machine learning |
| General subdivision | Ethics. |
| Topical term or geographic name as entry element | Data mining |
| General subdivision | Social aspects |
| Source of heading or term | fast |
| Authority record control number | (OCoLC)fst01983683 |
| Topical term or geographic name as entry element | Ethics |
| Source of heading or term | fast |
| Authority record control number | (OCoLC)fst00915833 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Franks, Bill, |
| Dates associated with a name | 1968- |
| Relator term | editor. |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) | |
| 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 | |
| Koha item type | Text Book |
| Price effective from | Date last seen | Permanent Location | Not for loan | Date acquired | Source of classification or shelving scheme | Koha item type | Lost status | Withdrawn status | Copy number | Source of acquisition | Collection code | Damaged status | Shelving location | Barcode | Current Location | Full call number |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-09-17 | 2022-09-17 | Institute of Information Sciences, Noakhali Science and Technology Uiversity | 2022-09-17 | Text Book | 1 | purchase from kollol Book | Non Fiction | 0000035 | Institute of Information Sciences, Noakhali Science and Technology Uiversity | 006.3/12 | ||||||
| 2022-09-17 | 2022-09-17 | Institute of Information Sciences, Noakhali Science and Technology Uiversity | 2022-09-17 | Text Book | 2 | purchase from kollol Book | Non Fiction | 0000034 | Institute of Information Sciences, Noakhali Science and Technology Uiversity | 006.3/12 |