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Machine Learning and Deterrence

Toronto, Canada 23 June 2022 – 25 June 2022

Henrik Sigstad (University of Oslo); Daniel L. Chen (Toulouse School of Economics)

C3 Algorithms
Chair: Arna Woemmel
Room J140
Economics / Institutions and organizations in the public sector

Abstract

What is the impact of artificial intelligence on the legal system? In a general deterrence model, we show that although machine learning might optimally reduce type I and type II errors, basing legal decisions on machine predictions can undermine incentives to abide by the law. We discuss under which assumptions machine learning methods can be adapted to obtain optimal deterrence. In a planned empirical application on a corpus of 14 million Brazilian labor lawsuits, we assess the amount of statistical discrimination under various machine learning decision rules.

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