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Prediction machines, insurance, and protection: An alternative perspective on AI’s role in production

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Editor's Note:

This is a Brookings Center on Regulation and Markets working paper.

Abstract

Recent advances in AI represent improvements in prediction. We examine how decisionmaking and risk management strategies change when prediction improves. The adoption of AI may cause substitution away from risk management activities used when rules are applied (rules require always taking the same action), instead allowing for decisionmaking (choosing actions based on the predicted state). We provide a formal model evaluating the impact of AI and how risk management, stakes, and interrelated tasks affect AI adoption. The broad conclusion is that AI adoption can be stymied by existing processes designed to address uncertainty. In particular, many processes are designed to enable coordinated decisionmaking among different actors in an organization. AI can make coordination even more challenging. However, when the cost of changing such processes falls, then the returns from AI adoption increase.

Download the full working paper here.


Avi Goldfarb is a consultant with Goldfarb Analytics Corporation, which advises organizations on digital and AI strategy. The authors did not receive financial support from any firm or person for this article or from any firm or person with a financial or political interest in this article. Other than the aforementioned, the authors are not currently an officer, director, or board member of any organization with a financial or political interest in this article.

Joshua S. Gans

Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship, Professor of Strategic Management - University of Toronto

Avi Goldfarb

Rotman Chair in Artificial Intelligence and Healthcare, and Professor of Marketing - Rotman School of Management at the University of Toronto

Chief Data Scientist - Creative Destruction Lab

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