  In this paper, 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 the researchers' intuition. Inspired by definitions and instruments for
assessing actionability in other domains, we construct a seven-question tool
and evaluate its effectiveness through two user studies. We show that the tool
discriminates actionability across explanation types and that the distinctions
align with human judgements. We show the impact of context on actionability
assessments, suggesting that domain-specific tool adaptations may foster more
human-centred algorithmic systems. This is a significant step forward for
research and practices into actionable explainability and algorithmic recourse,
providing the first clear human-centred definition and tool for assessing
actionability in explainable AI.
