Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals

TL;DR

This paper introduces LEWIS, a causality-based framework using probabilistic contrastive counterfactuals to generate effective, understandable explanations and recourse for black-box AI systems, outperforming existing methods.

Contribution

LEWIS is the first system to provide provably effective, scalable explanations and recourse at multiple levels without assuming internal model details, based solely on input-output data.

Findings

LEWIS outperforms LIME and SHAP in generating human-understandable explanations.

LEWIS provides effective recourse solutions that are scalable and correct.

Empirical results on real-world datasets validate LEWIS's effectiveness and scalability.

Abstract

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.