| Summary: | "Data science ethics is all about what is right and wrong when conducting data science. Data science has so far mainly been 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. This book looks at the different concepts and techniques related to data science ethics. Data scientists and business managers are not inherently unethical, but at the same time not trained to think this through either. This book aims to address this important gap. The techniques discussed range from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, measurements to assess, and techniques to remove discrimination against sensitive groups, and various explainable AI techniques. The real-life cautionary tales further illustrate the importance and potential impact of data science ethics, including tales of racist bots, search censoring, government backdoors, discrimination in recruitment, predicting pregnancy, redlining, re-identification of persons based on movie viewing and location data, cheating academics, and face recognition. Additionally, 10 discussions are provided with hypothetical scenarios, for example:'you are the founder of a startup ...', which then present an ethical dilemma related to data science. These can serve as structured exercises to be completed with your fellow colleagues, and will teach you how to balance the ethical concerns and the utility of your data"--Publisher's description.
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