Informative Semi-Factuals for XAI: The Elaborated Explanations that People Prefer

TL;DR

This paper introduces the informative semi-factuals (ISF) algorithm in XAI, which generates elaborated explanations by revealing hidden features influencing decisions, and demonstrates their effectiveness and user preference over simpler semi-factuals.

Contribution

The paper proposes the ISF algorithm that enhances semi-factual explanations with additional feature information, improving informativeness and user preference.

Findings

ISF produces high-quality, informative semi-factuals on benchmark datasets.

Users prefer elaborated explanations provided by ISF over simpler semi-factuals.

Experimental results confirm the effectiveness of the ISF method.

Abstract

Recently, in eXplainable AI (XAI), $\textit{even if}$ explanations -- so-called semi-factuals -- have emerged as a popular strategy that explains how a predicted outcome $\textit{can remain the same}$ even when certain input-features are altered. For example, in the commonly-used banking app scenario, a semi-factual explanation could inform customers about better options, other alternatives for their successful application, by saying "$\textit{Even if}$ you asked for double the loan amount, you would still be accepted". Most semi-factuals XAI algorithms focus on finding maximal value-changes to a single key-feature that do $\textit{not}$ alter the outcome (unlike counterfactual explanations that often find minimal value-changes to several features that alter the outcome). However, no current semi-factual method explains $\textit{why}$ these extreme value-changes do not alter outcomes; for example, a more informative semi-factual could tell the customer that it is their good credit score that allows them to borrow double their requested loan. In this work, we advance a new algorithm -- the $\textit{informative semi-factuals}$ (ISF) method -- that generates more elaborated explanations supplementing semi-factuals with information about additional $\textit{hidden features}$ that influence an automated decision. Experimental results on benchmark datasets show that this ISF method computes semi-factuals that are both informative and of high-quality on key metrics. Furthermore, a user study shows that people prefer these elaborated explanations over the simpler semi-factual explanations generated by current methods.