Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design

Faidra Monachou, Itai Ashlagi

Abstract: The increasing popularity of online two-sided markets such as ride-sharing, accommodation and freelance labor platforms, goes hand in hand with new socioeconomic challenges. A major issue remains the existence of bias and discrimination against certain social groups. We study this problem using a two-sided large market model of employers and workers mediated by a platform. Employers who seek to hire workers face uncertainty about a candidate worker's skill level.Therefore, they base their hiring decision on learning from past reviews about an individual worker as well as on their (possibly misspecified) prior beliefs about the ability level of the social group the worker belongs to. Drawing upon the social learning literature with bounded rationality and limited information, we find that uncertainty combined with social bias leads to unequal hiring opportunities between workers of different social groups. Consistent with empirical findings, we show that the effect of social bias decreases as the number of reviews increases. Furthermore, we quantify discrimination in terms of welfare inequality showing that minority workers have lower expected payoff. Finally, we assume a balanced market and design a simple directed matching policy (DM) which combines matching and learning to make better matching decisions for minority workers. We prove that there exists a steady-state equilibrium such that DM reduces the discrimination gap.

CMT Num: 1269