Algorithmic Decision-Making, Fairness, and the Distribution of Impact: Application to Refugee Matching

This paper proposes an approach to evaluating the group-level fairness of an algorithmic decision-making system on the basis of the distribution of causal impact, with an application to a new area of algorithmic decision-making in public policy that has received little attention in the algorithmic fairness literature: the geographic assignment of refugees within host countries. The approach formalizes the algorithmic assignment procedure and causal impact using the potential outcomes framework, and it offers flexibility to accommodate a wide range of use cases. Specifically, it is flexible in allowing for the consideration of outcomes of different types (continuous or discrete), impact on multiple outcomes of interest, any number of policy options to which units can be assigned (extending beyond binary decisions), and various ways in which predictions map to actual decisions. The paper illustrates the approach, as well as highlights the limits of conventional fairness perspectives, with an application to the geographic assignment of refugee. Real-world data on refugees in Sweden are used to evaluate the implications if refugees were algorithmically assigned to labor market regions to improve their employment outcomes, compared to the quasi-random status quo assignment, focusing particularly on fairness of the impact across gender. In addition to considering the algorithmic target outcome (i.e. employment), the proposed framework also facilitates evaluation of unintended impacts on “cross-outcomes” (e.g. skill development) and their implications for fairness.