Image from Google Jackets

Data science ethics : concepts, techniques and cautionary tales / David Martens.

By: Publisher: Oxford, United Kingdom : Oxford University Press, 2022Description: xii, 255 pages : illustrations (some color), color map ; 24 cmContent type:
  • text
  • still image
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780192847270
Subject(s): DDC classification:
  • 005.7 M36 23 2022
LOC classification:
  • QA76.9.B45 M36 2022
Contents:
Foster Provost: ForewordPreface1: Introduction to Data Science Ethics2: Ethical Data Gathering3: Ethical Data Preprocessing4: Ethical Modelling5: Ethical Evaluation6: Ethical Deployment7: Conclusion
Summary: 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. -- Provided by publisher.

Includes bibliographical references and index.

Foster Provost: ForewordPreface1: Introduction to Data Science Ethics2: Ethical Data Gathering3: Ethical Data Preprocessing4: Ethical Modelling5: Ethical Evaluation6: Ethical Deployment7: Conclusion

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. -- Provided by publisher.

College of Engineering and Computer Studies Bachelor of Science in Information Technology

Text in English

There are no comments on this title.

to post a comment.