An Actionability Assessment Tool for Enhancing Algorithmic Recourse in Explainable AI

Ronal Singh, Tim Miller, Liz Sonenberg, Eduardo Velloso, Frank Vetere, Piers Howe

Abstract: In this article, we introduce and evaluate a tool for researchers and practitioners to assess the actionability of information provided to users to support algorithmic recourse. While there are clear benefits of recourse from the user’s perspective, the notion of actionability in explainable AI research remains vague, and claims of ‘actionable’ explainability techniques are based on researchers’ intuitions. Inspired by definitions and instruments for assessing actionability in other domains, we construct a seven-item tool and investigate its effectiveness through two user studies. We show that the tool discriminates actionability across explanation types and that the distinctions align with human judgments. We illustrate the impact of context on actionability assessments, suggesting that domain-specific tool adaptations may foster more human-centred algorithmic systems. This is a valuable step forward for research and practices into actionable explainability and algorithmic recourse, providing the first clear human-centred tool for assessing actionability in explainable AI.