Computing Rule-Based Explanations by Leveraging Counterfactuals
This addresses the need for efficient and high-quality explanations in automated decision-making systems, such as loan applications, to increase user trust, though it is incremental by improving existing methods.
The paper tackles the inefficiency of computing rule-based explanations for high-stakes decisions by proposing a novel approach that leverages counterfactual explanations, proving a duality theorem and developing an efficient algorithm. Experiments show the system computes higher-quality explanations with equal or better performance compared to MinSetCover and Anchor.
Sophisticated machine models are increasingly used for high-stakes decisions in everyday life. There is an urgent need to develop effective explanation techniques for such automated decisions. Rule-Based Explanations have been proposed for high-stake decisions like loan applications, because they increase the users' trust in the decision. However, rule-based explanations are very inefficient to compute, and existing systems sacrifice their quality in order to achieve reasonable performance. We propose a novel approach to compute rule-based explanations, by using a different type of explanation, Counterfactual Explanations, for which several efficient systems have already been developed. We prove a Duality Theorem, showing that rule-based and counterfactual-based explanations are dual to each other, then use this observation to develop an efficient algorithm for computing rule-based explanations, which uses the counterfactual-based explanation as an oracle. We conduct extensive experiments showing that our system computes rule-based explanations of higher quality, and with the same or better performance, than two previous systems, MinSetCover and Anchor.