As machine learning algorithms move into realworld settings, it is crucial to ensure they are
aligned with societal values. There has been
much work on one aspect of this, namely the
discriminatory prediction problem: How can
we reduce discrimination in the predictions themselves? While an important question, solutions to
this problem only apply in a restricted setting, as
we have full control over the predictions. Often
we care about the non-discrimination of quantities we do not have full control over. Thus, we
describe another key aspect of this challenge, the
discriminatory impact problem: How can we
reduce discrimination arising from the real-world
impact of decisions? To address this, we describe
causal methods that model the relevant parts of
the real-world system in which the decisions are
made. Unlike previous approaches, these models not only allow us to map the causal pathway
of a single decision, but also to model the effect
of interference–how the impact on an individual
depends on decisions made about other people.
Often, the goal of decision policies is to maximize a beneficial impact overall. To reduce the
discrimination of these benefits, we devise a constraint inspired by recent work in counterfactual
fairness (Kusner et al., 2017), and give an efficient
procedure to solve the constrained optimization
problem. We demonstrate our approach with an
example: how to increase students taking college
entrance exams in New York City public schools
