Institutional Affiliation: Harvard University
|An Economic Approach to Regulating Algorithms|
with Jon Kleinberg, Sendhil Mullainathan, Jens Ludwig: w27111
There is growing concern about "algorithmic bias" - that predictive algorithms used in decision-making might bake in or exacerbate discrimination in society. When will these "biases" arise? What should be done about them? We argue that such questions are naturally answered using the tools of welfare economics: a social welfare function for the policymaker, a private objective function for the algorithm designer and a model of their information sets and interaction. We build such a model that allows the training data to exhibit a wide range of "biases." Prevailing wisdom is that biased data change how the algorithm is trained and whether an algorithm should be used at all. In contrast, we find two striking irrelevance results. First, when the social planner builds the algorithm, her equity ...