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20242026
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cs.AI2026

Shielding for Higher-Order Safety

Filip Cano, Thomas A. Henzinger, Konstantin Kueffner

Safety shields are runtime enforcement mechanisms that restrict the actions of a controller to guarantee safety. Classical shields are usually synthesised for state predicates: the…

cs.AI2026

Confidence Sequences for Online Statistical Model Checking of Markov Decision Processes

Konstantin Kueffner, Tobias Meggendorfer, Maximilian Weininger +1

Markov decision processes (MDPs) are a classic model of decision making under uncertainty, exhibiting both non-deterministic choice as well as probabilistic uncertainty. Traditiona…

cs.AI2026

Energy Shields for Fairness

Filip Cano, Thomas A. Henzinger, Konstantin Kueffner

Runtime fairness is not a one-time constraint but a dynamic property evaluated over a sequence of decisions. To ensure fairness at runtime, it is necessary to account for past deci…

cs.AI2025

Algorithmic Fairness: A Runtime Perspective

Filip Cano, Thomas A. Henzinger, Konstantin Kueffner

Fairness in AI is traditionally studied as a static property evaluated once, over a fixed dataset. However, real-world AI systems operate sequentially, with outcomes and environmen…

cs.AI2025

Monitoring Robustness and Individual Fairness

Ashutosh Gupta, Thomas A. Henzinger, Konstantin Kueffner +2

Input-output robustness appears in various different forms in the literature, such as robustness of AI models to adversarial or semantic perturbations and individual fairness of AI…

cs.AI2024

Fairness Shields: Safeguarding against Biased Decision Makers

Filip Cano, Thomas A. Henzinger, Bettina Könighofer +2

As AI-based decision-makers increasingly influence human lives, it is a growing concern that their decisions are often unfair or biased with respect to people's sensitive attribute…