1 citations · 3 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…
Multi-Environment POMDPs with Finite-Horizon Objectives
Léonard Brice, Filip Cano, Krishnendu Chatterjee +2
Partially Observable Markov Decision Processes (POMDPs) are systems in which one agent interacts with a stochastic environment, and receives only partial information about the curr…
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…
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…
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…