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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.
List of contents
- Foreword
- Preface
- 1: Introduction to Data Science Ethics
- 2: Ethical Data Gathering
- 3: Ethical Data Preprocessing
- 4: Ethical Modelling
- 5: Ethical Evaluation
- 6: Ethical Deployment
- 7: Conclusion
About the author
David Martens is Professor of Data Science at the Department of Engineering Management, University of Antwerp, Belgium. He teaches data mining and data science and ethics to postgraduate students studying business economics and business engineering. In his work, David has collaborated with large banks, insurance companies and telco companies, as well as with various technology startups. His research has been published in high-impact journals and has received several awards.
Summary
This book examines a variety of different concepts related to data science ethics and techniques that can help with, or lead to, ethical concerns, whilst featuring cautionary tales that illustrate the importance and potential impact of data science ethics.
Additional text
This is an important and timely book for data scientists, written in a clear and engaging way. Motivated by many relevant examples, the author successfully de-mystifies data ethics lingo and presents a comprehensive view of ethical considerations during the entire data science lifecycle.
