Computational Justice: Simulating Structural Bias and Interventions

Gender inequality has been documented across a variety of high-prestige professions. Both structural bias (e.g., lack of proportionate representation) and interpersonal bias (e.g., sexism, discrimination) generate costs to underrepresented minorities. How can we estimate these costs and what interventions are most effective for reducing them? We used agent-based simulations, removing gender differences in interpersonal bias to isolate and quantify the impact and costs of structural bias (unequal gender ratios) on individuals and institutions. We compared the long-term impact of bias-confrontation strategies. Unequal gender ratios led to higher costs for female agents and institutions and increased sexism among male agents. Confronting interpersonal bias by targets and allies attenuated the impact of structural bias. However, bias persisted even after a structural intervention to suddenly make previously unequal institutions equal (50% women) unless the probability of interpersonal bias-confrontation was further increased among targets and allies. This computational approach allows for comparison of various policies to attenuate structural equality, and informs the design of new experiments to estimate parameters for more accurate predictions.