Policy fairness and unknown bias dynamics in sequential allocations

This work considers a dynamic decision making framework for allocating opportunities over time to advantaged and disadvantaged individuals, focusing on the example of college admissions. Here, individuals in the disadvantaged group are assumed to experience a societal bias that limits their success probability. Bias dynamics dictate how the societal bias changes based on the current allocation of opportunities. We model this environment as a Markov Decision Process (MDP) and empirically examine the purely utility maximising policy in terms of fairness. We demonstrate the influence of the bias dynamics on long-term fairness of allocations, and analyse the interplay between utility and policy-fairness for different dynamics under different optimisation parameters. We consider the cases of known and unknown bias dynamics. For known dynamics, we show that a short horizon view presents fairness as a trade-off for utility, but a long horizon view reveals that the two are aligned. Moreover, we suggest that when the dynamics are unknown, the approach towards epistemic uncertainty may also affect fairness, and should be considered when designing fair decision making models