Algorithmic Fairness and the Social Welfare Function

Social scientists have long been interested in discrimination and other inherent social inequities; and as such have developed models to evaluate policies through the dual lenses of efficiency and equity. More recently, computer scientists have illustrated show algorithms in many domains inherit and (sometimes inadvertently) bake in these same human biases and inequities. In this talk, I attempt to bring these two strands together: I embed concerns about algorithmic bias within a broader welfare economics framework. Instead of viewing the data as given, it begins with a model of the underlying social phenomena and their accompanying inequities. It then posits a social welfare function, where the social planner cares both about efficiency and equity. In particular, she places greater weight on equity than individual algorithm designers (firms or citizens) do. Intrinsic to this approach is that the social planner's preferences imply desired properties of algorithm: the fairness of a given algorithm is not a primitive; instead, it is derived from the welfare of the outcomes it engenders. Several pieces of conventional wisdom do not hold true in this framework. For example, "blinding the algorithm" to variables such as race generally reduces welfare, even for the disadvantaged group. At the other extreme, I characterize situations where apparently fair algorithms can drastically increase inequities. Overall, I argue that it would be beneficial to model fairness and algorithmic bias more holistically, including both a generative model of the underlying social phenomena and a description of a global welfare function.