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The Ethical Algorithm: The Science of Socially Aware Algorithm Design

Product ID : 41913007


Galleon Product ID 41913007
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About The Ethical Algorithm: The Science Of Socially

Product Description 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. Review "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.