| 000 | 03035nam a22003977a 4500 | ||
|---|---|---|---|
| 999 |
_c128412 _d128412 |
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| 003 | phtghnu | ||
| 005 | 20231002101159.0 | ||
| 007 | ta | ||
| 008 | 210908s2022 enkab b 001 0 eng d | ||
| 010 | _a 2021946685 | ||
| 020 |
_a9780192847270 _qpaperback |
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| 035 | _a(OCoLC)on1273469063 | ||
| 040 |
_beng _cHNU _erda |
||
| 042 | _alccopycat | ||
| 050 | 0 | 0 |
_aQA76.9.B45 _bM36 2022 |
| 082 | 0 | 4 |
_a005.7 M36 _223 _3GC _b2022 |
| 100 | 1 |
_aMartens, David, _eauthor |
|
| 245 | 1 | 0 |
_aData science ethics : _bconcepts, techniques and cautionary tales / _cDavid Martens. |
| 264 | 1 |
_aOxford, United Kingdom : _bOxford University Press, _c2022. |
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| 300 |
_axii, 255 pages : _billustrations (some color), color map ; _c24 cm |
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| 336 |
_atext _2rdacontent |
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| 336 |
_astill image _2rdacontent |
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| 337 |
_aunmediated _2rdamedia |
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| 338 |
_avolume _2rdacarrier |
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| 504 | _aIncludes bibliographical references and index. | ||
| 505 | _aFoster Provost: ForewordPreface1: Introduction to Data Science Ethics2: Ethical Data Gathering3: Ethical Data Preprocessing4: Ethical Modelling5: Ethical Evaluation6: Ethical Deployment7: Conclusion | ||
| 520 |
_aData science ethics is all about what is right and wrong when conducting data science. Data science has so far been primarily used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences: an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. While data scientists and business managers are not inherently unethical, they are not trained to weigh the ethical considerations that come from their work - Data Science Ethics addresses this increasingly significant gap and highlights different concepts and techniques that aid understanding, ranging from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, techniques to remove discrimination against sensitive groups, and various explainable AI techniques. Real-life cautionary tales further illustrate the importance and potential impact of data science ethics, including tales of racist bots, search censoring, government backdoors, and face recognition. The book is punctuated with structured exercises that provide hypothetical scenarios and ethical dilemmas for reflection that teach readers how to balance the ethical concerns and the utility of data. -- _cProvided by publisher. |
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| 521 |
_aCOECS _bBachelor of Science in Information Technology |
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| 546 | _aText in English | ||
| 650 | 0 |
_aBig data _xMoral and ethical aspects. |
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| 650 | 0 |
_aData mining _xMoral and ethical aspects. |
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| 906 |
_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg |
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| 942 |
_2ddc _cBK _h000-099 |
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