The Societal Impacts of Algorithmic Decision-Making

Manish Raghavan

ISBN 13: 9798400708596

Verlag: ACM Books, 2023

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - This book demonstrates the need for and the value of interdisciplinary research in addressing important societal challenges associated with the widespread use of algorithmic decision-making. Algorithms are increasingly being used to make decisions in various domains such as criminal justice, medicine, and employment. While algorithmic tools have the potential to make decision-making more accurate, consistent, and transparent, they pose serious challenges to societal interests. For example, they can perpetuate discrimination, cause representational harm, and deny opportunities.The Societal Impacts of Algorithmic Decision-Making presents several contributions to the growing body of literature that seeks to respond to these challenges, drawing on techniques and insights from computer science, economics, and law. The author develops tools and frameworks to characterize the impacts of decision-making and incorporates models of behavior to reason about decision-making in complex environments. These technical insights are leveraged to deepen the qualitative understanding of the impacts of algorithms on problem domains including employment and lending.The social harms of algorithmic decision-making are far from being solved. While easy solutions are not presented here, there are actionable insights for those who seek to deploy algorithms responsibly. The research presented within this book will hopefully contribute to broader efforts to safeguard societal values while still taking advantage of the promise of algorithmic decision-making.

Bestandsnummer des Verkäufers 9798400708596

Bibliografische Details

• Titel: The Societal Impacts of Algorithmic Decision-Making
• Verlag: ACM Books
• Erscheinungsjahr: 2023
• Sprache: Englisch
• ISBN-13: 9798400708596
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 235B x 191 H x 20 T Millimeter Breite: 235 mm, Höhe: 191 mm, Tiefe: 20 mm
• Gewicht: 683 Gramm

Über diesen Titel

Inhaltsangabe

This book demonstrates the need for and the value of interdisciplinary research in addressing important societal challenges associated with the widespread use of algorithmic decision-making. Algorithms are increasingly being used to make decisions in various domains such as criminal justice, medicine, and employment. While algorithmic tools have the potential to make decision-making more accurate, consistent, and transparent, they pose serious challenges to societal interests. For example, they can perpetuate discrimination, cause representational harm, and deny opportunities.

The Societal Impacts of Algorithmic Decision-Making presents several contributions to the growing body of literature that seeks to respond to these challenges, drawing on techniques and insights from computer science, economics, and law. The author develops tools and frameworks to characterize the impacts of decision-making and incorporates models of behavior to reason about decision-making in complex environments. These technical insights are leveraged to deepen the qualitative understanding of the impacts of algorithms on problem domains including employment and lending.

The social harms of algorithmic decision-making are far from being solved. While easy solutions are not presented here, there are actionable insights for those who seek to deploy algorithms responsibly. The research presented within this book will hopefully contribute to broader efforts to safeguard societal values while still taking advantage of the promise of algorithmic decision-making.

Über die Autorin bzw. den Autor

Manish Raghavan is the Drew Houston (2005) Career Development Professor at the MIT Sloan School of Management and Department of Electrical Engineering and Computer Science. His research focuses on how algorithms and algorithmic decision-making impact society in a variety of contexts including employment and online media. He is the recipient of the 2021 ACM Doctoral Dissertation Award.