What If. . . Counterfactual Explanations Were to Be Deployed?
TL;DR
Three lines of work are considered that build on standard counterfactual explanations and ask what would need to change for them to be better aligned with deployment requirements, including one that extends them beyond one-shot decisions to capture sequential decision-making.
Abstract
Explainable AI is now a mature and rapidly expanding field, with a wide range of methods for interpreting models and explaining their behaviour. Despite this progress, many of these methods have yet to make their way into the engineering pipelines where AI is actually deployed. This gap suggests a question that is central to this paper: what would it take for explanations to be informative, useful and dependable when deployed? I approach this question through counterfactual explanations, which I view as a promising foundation for practical XAI: by showing what would need to change for an AI decision to differ, they are easy to interpret and carry actionable information. In their standard form, however, counterfactuals come with limitations of form and scope that constrain their applicability in deployment. I therefore consider three lines of work that build on standard counterfactual explanations and ask what would need to change for them to be better aligned with deployment requirements. The first concerns robustness, ensuring that counterfactuals remain valid under the perturbations that deployment introduces. The second extends them beyond one-shot decisions to capture sequential decision-making. The third takes counterfactuals as a starting point for contestability, arguing that meaningful contestation requires more than an explanation alone. I close by reflecting on energy systems as a critical testbed for explainability, one that highlights the promises and limitations of current methods and may offer a source of criteria for the next generation of explanation methods.