Hardback : £68.67
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.
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.
Foster Provost: 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
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.
An excellent reading with both depth and breadth on some of the
most important challenges and risks data scientists, businesses,
governments and societies face today as Artificial Intelligence
adoption grows. These are topics everyone needs to be aware of, and
this is one of the very few must read books on these issues
*Theodoros Evgeniou, Professor of Decision Sciences and Technology
Management at INSEAD, France*
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.
*Galit Shmueli, Tsing Hua Distinguished Professor, Institute of
Service Science and Institute Director, College of Technology
Management, National Tsing Hua University, Taiwan*
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