Algorithmic Discrimination Is an Information Problem

56 Pages Posted: 7 Jun 2019 Last revised: 24 Jun 2020

See all articles by Ignacio Cofone

Ignacio Cofone

McGill University Faculty of Law

Date Written: May 14, 2019


Increasingly, algorithms make socially consequential decisions. This makes it crucial to determine when their decisions are discriminatory and how to prevent this outcome. While algorithmic decision-making has proven to be a challenge for traditional anti-discrimination law, there is an opportunity to regulate algorithms through the information that they are fed. But blocking information about protected categories will rarely protect these groups effectively because other information will act as proxies. To avoid disparate treatment, the protected category attributes cannot be considered; but to avoid disparate impact, they must be considered. This leads to a paradox in regulating information to prevent algorithmic discrimination. This paper addresses this problem. It suggests that, instead of blocking or allowing attributes in training data, we should modify them.

Keywords: algorithmic discrimination, algorithmic decision-making, discrimination, machine learning, artificial intelligence, AI, COMPAS, criminal procedure, employment discrimination, privacy, data protection

Suggested Citation

Cofone, Ignacio, Algorithmic Discrimination Is an Information Problem (May 14, 2019). 70 Hastings Law Journal 1389 (2019), Available at SSRN:

Ignacio Cofone (Contact Author)

McGill University Faculty of Law ( email )

3644 Peel Street
Montreal H3A 1W9, Quebec


Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
PlumX Metrics