  A significant body of research in the data sciences considers unfair
discrimination against social categories such as race or gender that could
occur or be amplified as a result of algorithmic decisions. Simultaneously,
real-world disparities continue to exist, even before algorithmic decisions are
made. In this work, we draw on insights from the social sciences brought into
the realm of causal modeling and constrained optimization, and develop a novel
algorithmic framework for tackling pre-existing real-world disparities. The
purpose of our framework, which we call the "impact remediation framework," is
to measure real-world disparities and discover the optimal intervention
policies that could help improve equity or access to opportunity for those who
are underserved with respect to an outcome of interest. We develop a
disaggregated approach to tackling pre-existing disparities that relaxes the
typical set of assumptions required for the use of social categories in
structural causal models. Our approach flexibly incorporates counterfactuals
and is compatible with various ontological assumptions about the nature of
social categories. We demonstrate impact remediation with a hypothetical case
study and compare our disaggregated approach to an existing state-of-the-art
approach, comparing its structure and resulting policy recommendations. In
contrast to most work on optimal policy learning, we explore disparity
reduction itself as an objective, explicitly focusing the power of algorithms
on reducing inequality.

Machine Learning;Artificial Intelligence;Computers and Society;Applications;Machine Learning