Data Science Ethics: Concepts, Techniques, and Cautionary Tales

Data science ethics is a growing field that focuses on the ethical considerations of data science, including privacy invasion, data-driven discrimination, and decision-making by complex models. This book provides concepts and techniques to address these issues, such as k-anonymity, differential privacy, homomorphic encryption, and zero-knowledge proofs, and real-life cautionary tales to illustrate the importance of data science ethics.

Format: Paperback / softback
Length: 272 pages
Publication date: 24 March 2022
Publisher: Oxford University Press

Data science has brought about significant advancements in businesses and society, but it has also raised ethical concerns. Privacy invasion, data-driven discrimination against sensitive groups, and decision-making by complex models without explanations are some of the negative consequences of data science. While data scientists and business managers are not inherently unethical, they need to be trained to weigh the ethical considerations that come with their work.

Data science ethics encompasses a range of concepts and techniques to address these ethical concerns. K-anonymity and differential privacy are used to protect individuals' privacy by ensuring that their data is not identifiable. Homomorphic encryption and zero-knowledge proofs are used to secure sensitive data while allowing for analysis. Explainable AI techniques are used to make machine learning models more transparent and understandable.

Real-life cautionary tales further illustrate the importance and potential impact of data science ethics. Racist bots, search censoring, government backdoors, and face recognition are examples of unethical practices that have occurred due to the misuse of data science. These tales serve as a reminder of the need for ethical considerations in data science.

The book "Data Science Ethics" provides a comprehensive guide to understanding and addressing ethical concerns in data science. It covers various topics, including privacy, discrimination, and explainability. Structured exercises are included to provide practical examples and ethical dilemmas for reflection. By reading this book, readers can develop the skills necessary to balance the ethical considerations and the utility of data in their work.

In conclusion, data science ethics is crucial for ensuring that data science is used for the benefit of businesses and society. By implementing ethical principles and techniques, we can mitigate the negative consequences of data science and create a more responsible and ethical data-driven world.

Weight: 466g
Dimension: 156 x 234 x 22 (mm)
ISBN-13: 9780192847270