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The Ethical Algorithm: The Science of Socially Aware Algorithm Design Illustrated Edition

by Michael Kearns (Author), Aaron Roth (Author)
4.6 out of 5 stars 166 ratings
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  1. ISBN-10
    0190948205
  2. ISBN-13
    978-0190948207
  3. Edition
    Illustrated
  4. Publisher
    Oxford University Press
  5. Publication date
    November 1, 2019
  6. Language
    English
  7. Dimensions
    9.3 x 0.9 x 6.1 inches
  8. Print length
    232 pages
  9. See all details
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From the Publisher

Editorial Reviews

Review


"...the authors take us on a journey through the main socio-algorithmic problems representing social constraints upon algorithms, their consequences and trade-offs. [T]hey provide concrete technical solutions for the challenges discussed throughout the book's 5 chapters: privacy, fairness, user-data-algorithm feedback loop, data-driven scientific discoveries and (brief) thoughts on the ethical issues (transparency, accountability, morality) yet to be pursued scientifically." -- ESSSAT News & Reviews


"It is elegantly written, easy to read and full of entertaining examples." -- Martin Peterson, Prometheus


"The data science revolution has important ethical underpinnings. The authors make a powerful case for the development of AI ethics as a subject in its own right. The Ethical Algorithm shows that apocalyptic predictions of an algorithmized society are far from inevitable. Anyone interested in AI should read and process this essential book."
--Dr. Marcos Lopez de Prado, C.E.O. of True Positive Technologies, L.P., and Quant of the Year 2019 (The Journal of Portfolio Management)


"Is the road to hell paved with well-intentioned algorithms? Many think so. For a more level-headed view, read this timely book by two leading experts: clear, concise, focused on understanding the problems and figuring out solutions, it's your perfect guide to the new science of ethical algorithms."
--Dr. Pedro Domingos, Professor of Computer Science at the University of Washington and Author of The Master Algorithm


"Can ethics ever be 'encoded' into algorithms? In this very timely book, Michael Kearns and Aaron Roth present a range of algorithmic solutions to problems seemingly inherent to algorithmic decision-making. In a very methodical yet entertaining manner, they highlight the potential of fixing issues related to privacy, fairness, and interpretability within algorithms without losing sight of the ongoing importance of human judgment."
--Dr. Dorothea Baur, Author and International Technology Ethics Consultant


"In a world with unprecedented access to data, this well researched book tackles the near term risks with concrete, real world examples that are facing algorithms today. The Ethical Algorithm is clearly articulated and full of insights."
--Carol E. Reiley, Cofounder, Advisor, and Board Member of Drive.ai


"Both Kearns and Roth, professors of computer and information science at the University of Pennsylvania, who have written extensively on algorithms and data science, here do a thorough job of explaining the science of algorithm design and the ethical and inherent technical difficulties in doing so."--Library Journal


"If you are interested in your online footprint, or just confused about the adverts you receive, I cannot recommend The Ethical Algorithm enough. It is a thought-provoking yet easy read, demystifies the processing of large datasets and neatly lays out the power -- and ultimately the limits -- of designing more responsible machine-learning algorithms."

--Nature Physics


"Recommended for anyone with an interest in technology and the ethical implications of our increasing use of algorithms."--Andrei Rogobete, The Centre for Enterprise, Markets, and Ethics


About the Author


Michael Kearns is Professor and the National Center Chair in the Computer and Information Science department of the University of Pennsylvania, where he has secondary appointments in Economics and the Wharton School. He is also the Founding Director of Penn's Warren Center for Network and Data Sciences. Kearns has published widely in machine learning, artificial intelligence, algorithmic game theory and quantitative finance. He has worked extensively in the finance and technology industries, and consulted on various legal and regulatory matters involving algorithms, data, and machine learning. Together with U.V. Vazirani, he is the author of An Introduction to Computational Learning Theory.

Aaron Roth is the class of 1940 Bicentennial Term Associate Professor in the Computer and Information Science department at the University of Pennsylvania, where he co-directs Penn's program in Networked and Social Systems Engineering. Roth has published widely in algorithms, machine learning, data privacy, and algorithmic game theory, and has consulted extensively about algorithmic privacy. He is the recipient of numerous awards, including a Presidential Early Career Award for Scientists and Engineers (PECASE) awarded by President Obama in 2016. Together with Cynthia Dwork, he is the author of The Algorithmic Foundations of Differential Privacy.

Product details

  • Publisher ‏ : ‎ Oxford University Press; Illustrated edition (November 1, 2019)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 232 pages
  • ISBN-10 ‏ : ‎ 0190948205
  • ISBN-13 ‏ : ‎ 978-0190948207
  • Item Weight ‏ : ‎ 1.06 pounds
  • Dimensions ‏ : ‎ 9.3 x 0.9 x 6.1 inches
  • Customer Reviews:
    4.6 out of 5 stars 166 ratings

About the author

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Aaron Roth

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Aaron Roth is a Professor in the Computer and Information Science department at the University of Pennsylvania, where he co-directs Penn's program in Networked and Social Systems Engineering. Roth has published widely in algorithms, machine learning, data privacy, and algorithmic game theory, and has consulted extensively about algorithmic privacy. He is the recipient of numerous awards, including a Presidential Early Career Award for Scientists and Engineers (PECASE) awarded by President Obama in 2016. Together with Cynthia Dwork, he is the author of The Algorithmic Foundations of Differential Privacy.

Customer reviews

4.6 out of 5 stars
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John
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5.0 out of 5 stars An amazing theoretical insight in ethical algorithmic design
Reviewed in the United States 🇺🇸 on April 24, 2022
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John
5.0 out of 5 stars An amazing theoretical insight in ethical algorithmic design
Reviewed in the United States 🇺🇸 on April 24, 2022
I did not study computer science, though I do work as a data analyst. I’ve learned a lot of new things from this book, many of the constraints the author touched on in the book, such as: fairness, k-anonymity, differential privacy, even the process of analysing data for research have really changed the way I view my work in terms of ethical data handling, and the process in which I conduct my analysis.

It’s an amazing for computer scientist, software developers, anyone who works with data, and even policy makers or tech enthusiasts.
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4.0 out of 5 stars Digestible, thorough summary of a complicated issue
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5.0 out of 5 stars There is a lot to unpack here!
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2.0 out of 5 stars What's interesting isn't new, and what's new isn't interesting
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5.0 out of 5 stars An Important Book for Our Time
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4.0 out of 5 stars A good introductory book
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5.0 out of 5 stars Great read for a non-computer science person
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Harry Hughes
5.0 out of 5 stars Brilliantly done!
Reviewed in the United Kingdom 🇬🇧 on November 14, 2021
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5.0 out of 5 stars I learned a lot about ML, privacy and ethical issues
Reviewed in Germany 🇩🇪 on November 14, 2020
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5.0 out of 5 stars Informative and interesting read
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4.0 out of 5 stars I expected more practical implications, but there aren’t now
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Edgar
5.0 out of 5 stars We should all be reading this!
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