Data Science Ethics - Concepts, Techniques, and Cautionary Tales

[ DevCourseWeb.com ] Data Science Ethics: Concepts, Techniques, and Cautionary Tales



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English | 2022 | ISBN: ‎ 0192847260, 978-0192847270 | 273 pages | True PDF | 19.9 MB



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.



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