| 000 -LEADER |
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| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230913b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780192847270 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
005.7 |
| Item number |
MAR |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Martens, David |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Data science ethics : concepts, techniques and cautionary tales |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc |
Oxford University Press, |
| Date of publication, distribution, etc |
2022 |
| Place of publication, distribution, etc |
Oxford : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xii, 255 p. ; |
| Other physical details |
ill., (some col.), |
| Dimensions |
24 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
33.49 |
| Price type code |
GBP |
| Unit of pricing |
109.80 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
Data 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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining |
|
|---|
| Topical term or geographic name as entry element |
Big data moral |
|
|---|
| Topical term or geographic name as entry element |
Prediction model |
|
|---|
| Topical term or geographic name as entry element |
NASDAQ |
|
|---|
| Topical term or geographic name as entry element |
Deployment |
|
|---|
| Topical term or geographic name as entry element |
Evaluation |
|
|---|
| Topical term or geographic name as entry element |
Modelling |
|
|---|
| Topical term or geographic name as entry element |
Data preprocessing |
|
|---|
| Topical term or geographic name as entry element |
Ethical data gathering |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Books |