00000nam a2200000 i 4500 UP-8027295163992797889 Buklod 20241017141838.0 m |o d | cr ||||||||||| 240815s2022 enk ab b 001 0 eng d 9780192847270 paperback 22225173 DLC DLS rda eng QA 76.9 B45 M36 2022 Martens, David author. Data science ethics concepts, techniques and cautionary tales David Martens. Oxford, United Kingdom Oxford University Press [2022] xiv, 255 pages illustrations (some color), color map 24 cm. text rdacontent unmediated rdamedia volume rdacarrier Includes bibliographical references and index. 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. -- Provided by publisher. Big data Moral and ethical aspects. Data mining Moral and ethical aspects. FO UPD DLS QA 76.9 B45 M36 2022 Book