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Over The Course Of A Generation, Algorithms Have Gone From Mathematical Abstractions To Powerful Mediators Of Daily Life. Algorithms Have Made Our Lives More Efficient, More Entertaining, And, Sometimes, Better Informed. At The Same Time, Complex Algorithms Are Increasingly Violating The Basic Rights Of Individual Citizens. Allegedly Anonymized Datasets Routinely Leak Our Most Sensitive Personal Information; Statistical Models For Everything From Mortgages To College Admissions Reflect Racial And Gender Bias. Meanwhile, Users Manipulate Algorithms To Game Search Engines, Spam Filters, Online Reviewing Services, And Navigation Apps. Understanding And Improving The Science Behind The Algorithms That Run Our Lives Is Rapidly Becoming One Of The Most Pressing Issues Of This Century. Traditional Fixes, Such As Laws, Regulations And Watchdog Groups, Have Proven Woefully Inadequate. Reporting From The Cutting Edge Of Scientific Research, The Ethical Algorithm Offers A New Approach: A Set Of Principled Solutions Based On The Emerging And Exciting Science Of Socially Aware Algorithm Design. Michael Kearns And Aaron Roth Explain How We Can Better Embed Human Principles Into Machine Code - Without Halting The Advance Of Data-driven Scientific Exploration. Weaving Together Innovative Research With Stories Of Citizens, Scientists, And Activists On The Front Lines, The Ethical Algorithm Offers A Compelling Vision For A Future, One In Which We Can Better Protect Humans From The Unintended Impacts Of Algorithms While Continuing To Inspire Wondrous Advances In Technology.
How can we mathematically embed human ethical principles into the design of machine learning algorithms to prevent bias and protect individual rights? The authors, both distinguished computer scientists, argue that traditional regulatory approaches are insufficient for the scale of modern data-driven systems. They propose a framework rooted in 'socially aware algorithm design,' which treats fairness, privacy, and transparency as technical constraints rather than afterthoughts. By integrating these principles directly into the code, they suggest that technological progress can continue without compromising societal values.
What You Will Find
Scope Limits
Experts in the field of computer science recognize this work as a foundational text for understanding the technical challenges of algorithmic fairness. Readers frequently note that while the prose is accessible to a general audience, it maintains a high level of technical rigor regarding the mathematical trade-offs involved in machine learning design.
Page Count:
288
Publication Date:
2019-01-01
Publisher:
Oxford University Press
ISBN-10:
0190948213
ISBN-13:
9780190948214
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