Affirmative Action vs. Affirmative Information

TL;DR

This paper analyzes how prediction errors differ across demographic groups due to uncertainty and proposes 'Affirmative Information' as a data acquisition strategy to mitigate disparities, offering an alternative to traditional Affirmative Action.

Contribution

It identifies systematic error patterns in decision-making across groups and introduces 'Affirmative Information' as a novel approach to reduce disparities.

Findings

Higher average outcomes lead to higher false positive rates.

Lower average outcomes lead to higher false negative rates.

Data omission does not fix error disparities.

Abstract

Critical decisions in hiring, college admissions, and credit lending are guided by predictions made in the presence of uncertainty. While uncertainty imparts errors across all demographic groups, this paper shows that the types of errors vary systematically: Groups with higher average outcomes are typically assigned higher false positive rates, while those with lower average outcomes are assigned higher false negative rates. We characterize the conditions that give rise to this disparate impact and explain why the intuitive remedy to omit demographic variables from datasets does not correct it. Instead of data omission, this paper examines how data acquisition can broaden access to opportunity. The strategy, which we call "Affirmative Information," could stand as an alternative to Affirmative Action.