  As predictive models are increasingly being deployed in high-stakes
decision-making, there has been a lot of interest in developing algorithms
which can provide recourses to affected individuals. While developing such
tools is important, it is even more critical to analyse and interpret a
predictive model, and vet it thoroughly to ensure that the recourses it offers
are meaningful and non-discriminatory before it is deployed in the real world.
To this end, we propose a novel model agnostic framework called Actionable
Recourse Summaries (AReS) to construct global counterfactual explanations which
provide an interpretable and accurate summary of recourses for the entire
population. We formulate a novel objective which simultaneously optimizes for
correctness of the recourses and interpretability of the explanations, while
minimizing overall recourse costs across the entire population. More
specifically, our objective enables us to learn, with optimality guarantees on
recourse correctness, a small number of compact rule sets each of which capture
recourses for well defined subpopulations within the data. We also demonstrate
theoretically that several of the prior approaches proposed to generate
recourses for individuals are special cases of our framework. Experimental
evaluation with real world datasets and user studies demonstrate that our
framework can provide decision makers with a comprehensive overview of
recourses corresponding to any black box model, and consequently help detect
undesirable model biases and discrimination.
