000			a
999			_c32165 _d32165
008			230913b xxu||||| |||| 00| 0 eng d
020			_a9780192847270
082			_a005.7 _bMAR
100			_aMartens, David
245			_aData science ethics : concepts, techniques and cautionary tales
260			_bOxford University Press, _c2022 _aOxford :
300			_axii, 255 p. ; _bill., (some col.), _c24 cm
365			_b33.49 _cGBP _d109.80
504			_aIncludes bibliographical references and index.
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.
650			_aData mining
650			_aBig data moral
650			_aPrediction model
650			_aNASDAQ
650			_aDeployment
650			_aEvaluation
650			_aModelling
650			_aData preprocessing
650			_aEthical data gathering
942			_2ddc _cBK