Algorithmic Greenlining: An Approach to Increase Diversity - Borgs, Christian; Chayes, Jennifer; Haghtalab, Nika; Kalai, Adam Tauman; Vitercik, Ellen (Proceedings of the 2nd ACM Conference on AI, Ethics, and Society (AIES 2019), 2019)
In contexts such as college admissions, hiring, and image search, decision-makers often aspire to formulate selection criteria that yield both high-quality and diverse results. However, simultaneously optimizing for quality and diversity can be challenging, especially when the decision-maker does not know the true quality of any criterion and instead must rely on heuristics and intuition. We introduce an algorithmic framework that takes as input a user’s selection criterion, which may yield high-quality but homogeneous results. Using an application-specific notion of substitutability, our algorithms suggest similar criteria with more diverse results, in the spirit of statistical or demographic parity. For instance, given the image search query “chairman”, it suggests alternative queries which are similar but more gender-diverse, such as “chairperson”. In the context of college admissions, we apply our algorithm to a dataset of students’ applications and rediscover Texas’s “top 10% rule”: the input criterion is an ACT score cutoff, and the output is a class rank cutoff, automatically accepting the students in the top decile of their graduating class. Historically, this policy has been effective in admitting students who perform well in college and come from diverse backgrounds. We complement our empirical analysis with learning-theoretic guarantees for estimating the true diversity of any criterion based on historical data.