Kofi Hair-Ralston

This paper proposes an "informational" theory of discrimination, modeling how profit-maximizing firms may optimally choose a positive level of bias in algorithms, even with ex-ante identical protected groups and costless debiasing, to preserve informative signals. It argues that this private choice of bias is socially inefficient.

This paper introduces the Equitable Financial Inclusion Score (EFIS) to address challenges in traditional credit scoring for underserved populations. EFIS uses mathematical components like Kalman filtering, network theory, game-theoretic mechanisms, and behavioral profiling to transform excluded populations into visible, scoreable risks.

A book authored by Kofi Hair-Ralston sharing insights and wisdom gained from his experience as a young entrepreneur, particularly through his venture BLTMR.