3 citations · 4 across the 4 of their papers we have counts for
4 papers
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…
Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning
Stefan Pranger, Hana Chockler, Martin Tappler +1
In many Deep Reinforcement Learning (RL) problems, decisions in a trained policy vary in significance for the expected safety and performance of the policy. Since RL policies are v…
Automata Learning meets Shielding
Martin Tappler, Stefan Pranger, Bettina Könighofer +3
Safety is still one of the major research challenges in reinforcement learning (RL). In this paper, we address the problem of how to avoid safety violations of RL agents during exp…
Correct-by-Construction Runtime Enforcement in AI -- A Survey
Bettina Könighofer, Roderick Bloem, Rüdiger Ehlers +1
Runtime enforcement refers to the theories, techniques, and tools for enforcing correct behavior with respect to a formal specification of systems at runtime. In this paper, we are…