Algorithmic Bias and the New Chicago School

TL;DR

This paper explores how indirect regulation strategies, based on the New Chicago School framework, can effectively address algorithmic bias in AI systems through a combination of architecture, norms, market, and law.

Contribution

It introduces a novel regulatory approach that combines direct and indirect methods to mitigate algorithmic bias, diverging from traditional legal regulation.

Findings

Effective regulation requires a mixture of direct and indirect strategies.

Indirect regulation through architecture and norms can complement legal measures.

A balanced approach enhances fairness and reduces bias in AI systems.

Abstract

AI systems are increasingly deployed in both public and private sectors to independently make complicated decisions with far-reaching impact on individuals and the society. However, many AI algorithms are biased in the collection or processing of data, resulting in prejudiced decisions based on demographic features. Algorithmic biases occur because of the training data fed into the AI system or the design of algorithmic models. While most legal scholars propose a direct-regulation approach associated with the right of explanation or transparency obligation, this article provides a different picture regarding how indirect regulation can be used to regulate algorithmic bias based on the New Chicago School framework developed by Lawrence Lessig. This article concludes that an effective regulatory approach toward algorithmic bias will be the right mixture of direct and indirect regulations through architecture, norms, market, and the law.