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by Michael Kearns (Author), Aaron Roth (Author)
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.Author Biography
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.
