Explainability requirements as hyperproperties

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.

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Publication Details

Journal
Acta Informatica
Published
2025-10-13
DOI
https://doi.org/10.1007/s00236-025-00507-w
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
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article

Explainability requirements as hyperproperties

Bernd Finkbeiner, Julian Siber
Acta Informatica
Explainable Artificial Intelligence (XAI)
article

Explainability requirements as hyperproperties

Bernd Finkbeiner, Julian Siber
article en

Abstract

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.

Acta InformaticaVol. 62(4)
Helmholtz Center for Information Security (DE)
Openalex Percentile: Top 85%
Explainable Artificial Intelligence (XAI)
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