Explainability Requirements as Hyperproperties
Bernd Finkbeiner, Julian Siber
TL;DR
This work formalizes explainability as a hyperproperty for multi-agent systems by integrating Lewis-style counterfactuals, temporal logic, and knowledge into a unified framework. The authors introduce ICE, WCE, and GCE to capture internal, weak, and external facets of explainability, and demonstrate how these notions can be embedded into a hyperlogic whose model checking is decidable via translation to $FO[<,E]$ and HyperQPTL. A concrete hiring-system example illustrates how the framework distinguishes explainable from unexplainable behaviors and how observation and similarity relations drive agent-specific explanations. The paper establishes a rigorous taxonomy of explainability, proves decidability for finite-state models, and outlines a path toward practical verification tools, with future work on minimality constraints and probabilistic extensions. Overall, it provides a foundational formal theory and a viable verification route for explainability requirements in autonomous multi-agent systems.
Abstract
Explainability is emerging as a key requirement for autonomous systems. While many works have focused on what constitutes a valid explanation, few have considered formalizing explainability as a system property. In this work, we approach this problem from the perspective of hyperproperties. We start with a combination of three prominent flavors of modal logic and show how they can be used for specifying and verifying counterfactual explainability in multi-agent systems: With Lewis' counterfactuals, linear-time temporal logic, and a knowledge modality, we can reason about whether agents know why a specific observation occurs, i.e., whether that observation is explainable to them. We use this logic to formalize multiple notions of explainability on the system level. We then show how this logic can be embedded into a hyperlogic. Notably, from this analysis we conclude that the model-checking problem of our logic is decidable, which paves the way for the automated verification of explainability requirements.
