19 citations · 23 across the 11 of their papers we have counts for
14 papers
Runtime Monitoring of Supermartingale Certificates
Mirco Giacobbe, Thomas A. Henzinger, Konstantin Kueffner +2
Supermartingale certificates formally guarantee temporal properties of discrete and continuous-time stochastic dynamical systems. However, these guarantees rely on a mathematical m…
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…
Monitoring Discounted Sum Properties
Filip Cano, Thomas A. Henzinger, Konstantin Kueffner +1
Runtime monitoring of quantitative signals faces a fundamental trade-off between volatility and over-aggregation: instantaneous observations are noisy, while long-run averages obsc…
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…
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…
Alignment Monitoring
Thomas A. Henzinger, Konstantin Kueffner, Vasu Singh +1
Formal verification provides assurances that a probabilistic system satisfies its specification--conditioned on the system model being aligned with reality. We propose alignment mo…
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…
Formal Verification of Neural Certificates Done Dynamically
Thomas A. Henzinger, Konstantin Kueffner, Emily Yu
Neural certificates have emerged as a powerful tool in cyber-physical systems control, providing witnesses of correctness. These certificates, such as barrier functions, often lear…
Monitoring of Static Fairness
Thomas A. Henzinger, Mahyar Karimi, Konstantin Kueffner +1
Machine-learned systems are in widespread use for making decisions about humans, and it is important that they are fair, i.e., not biased against individuals based on sensitive att…
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…
Monitoring Algorithmic Fairness under Partial Observations
Thomas A. Henzinger, Konstantin Kueffner, Kaushik Mallik
As AI and machine-learned software are used increasingly for making decisions that affect humans, it is imperative that they remain fair and unbiased in their decisions. To complem…
Monitoring Algorithmic Fairness
Thomas A. Henzinger, Mahyar Karimi, Konstantin Kueffner +1
Machine-learned systems are in widespread use for making decisions about humans, and it is important that they are fair, i.e., not biased against individuals based on sensitive att…
Runtime Monitoring of Dynamic Fairness Properties
Thomas A. Henzinger, Mahyar Karimi, Konstantin Kueffner +1
A machine-learned system that is fair in static decision-making tasks may have biased societal impacts in the long-run. This may happen when the system interacts with humans and fe…