Affirmative Action vs. Affirmative Information
This addresses fairness issues in algorithmic decision-making for hiring, admissions, and lending, offering a novel data acquisition strategy rather than incremental adjustments.
The paper tackles the problem of systematic error disparities in predictive decisions like hiring and admissions, showing that groups with higher average outcomes face higher false positive rates while those with lower outcomes face higher false negative rates, and proposes 'Affirmative Information' as an alternative to Affirmative Action to broaden access to opportunity.
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.