Fairness and Machine Learning
Limitations and Opportunities
$172.52
- Hardcover
320 pages
- Release Date
3 January 2024
Summary
An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning.
Fairness and Machine Learning introduces advanced undergraduate and graduate students to the intellectual foundations of this recently emergent field, drawing on a diverse range of disciplinary perspectives to identify the opportunities and hazards of automated decision-making. It surveys the risks in many applications of machine learning and provides a review of …
Book Details
| ISBN-13: | 9780262048613 |
|---|---|
| ISBN-10: | 0262048612 |
| Author: | Moritz Hardt, Solon Barocas |
| Publisher: | MIT Press Ltd |
| Imprint: | MIT Press |
| Format: | Hardcover |
| Number of Pages: | 320 |
| Release Date: | 3 January 2024 |
| Weight: | 567g |
| Dimensions: | 229mm x 178mm |
| Series: | Adaptive Computation and Machine Learning series |
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About The Author
Moritz Hardt
Solon Barocas is a Principal Researcher in the New York City lab of Microsoft Research, where he is a member of the Fairness, Accountability, Transparency, and Ethics in AI (FATE) research group. He is an Adjunct Assistant Professor in the Department of Information Science at Cornell University and Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University.
Moritz Hardt is Director of Social Foundations of Computation at the Max Planck Institute for Intelligent Systems and coauthor of Patterns, Predictions, and Actions- Foundations of Machine Learning.
Arvind Narayanan is Professor of Computer Science at Princeton University and director of the Center for Information Technology Policy. His work was among the first to show how machine learning reflects cultural stereotypes, and he led the Princeton Web Transparency and Accountability Project to uncover how companies collect and use our personal information.
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