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As predictive models are increasingly being deployed in high-stakesdecision-making, there has been a lot of interest in developing algorithmswhich can provide recourses to affected individuals. While developing suchtools is important, it is even more critical to analyse and interpret apredictive model, and vet it thoroughly to ensure that the recourses it offersare meaningful and non-discriminatory before it is deployed in the real world.To this end, we propose a novel model agnostic framework called ActionableRecourse Summaries (AReS) to construct global counterfactual explanations whichprovide an interpretable and accurate summary of recourses for the entirepopulation. We formulate a novel objective which simultaneously optimizes forcorrectness of the recourses and interpretability of the explanations, whileminimizing overall recourse costs across the entire population. Morespecifically, our objective enables us to learn, with optimality guarantees onrecourse correctness, a small number of compact rule sets each of which capturerecourses for well defined subpopulations within the data. We also demonstratetheoretically that several of the prior approaches proposed to generaterecourses for individuals are special cases of our framework. Experimentalevaluation with real world datasets and user studies demonstrate that ourframework can provide decision makers with a comprehensive overview ofrecourses corresponding to any black box model, and consequently help detectundesirable model biases and discrimination.

Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses