  There has been a recent resurgence of interest in explainable artificial
intelligence (XAI) that aims to reduce the opaqueness of AI-based
decision-making systems, allowing humans to scrutinize and trust them. Prior
work in this context has focused on the attribution of responsibility for an
algorithm's decisions to its inputs wherein responsibility is typically
approached as a purely associational concept. In this paper, we propose a
principled causality-based approach for explaining black-box decision-making
systems that addresses limitations of existing methods in XAI. At the core of
our framework lies probabilistic contrastive counterfactuals, a concept that
can be traced back to philosophical, cognitive, and social foundations of
theories on how humans generate and select explanations. We show how such
counterfactuals can quantify the direct and indirect influences of a variable
on decisions made by an algorithm, and provide actionable recourse for
individuals negatively affected by the algorithm's decision. Unlike prior work,
our system, LEWIS: (1)can compute provably effective explanations and recourse
at local, global and contextual levels (2)is designed to work with users with
varying levels of background knowledge of the underlying causal model and
(3)makes no assumptions about the internals of an algorithmic system except for
the availability of its input-output data. We empirically evaluate LEWIS on
three real-world datasets and show that it generates human-understandable
explanations that improve upon state-of-the-art approaches in XAI, including
the popular LIME and SHAP. Experiments on synthetic data further demonstrate
the correctness of LEWIS's explanations and the scalability of its recourse
algorithm.
