28 citations · 73 across the 18 of their papers we have counts for
5 papers · 1 filter
Auction-Based Scheduling
Guy Avni, Kaushik Mallik, Suman Sadhukhan
Many sequential decision-making tasks require satisfaction of multiple, partially contradictory objectives. Existing approaches are monolithic, namely all objectives are fulfilled…
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…
Computing Adequately Permissive Assumptions for Synthesis
Ashwani Anand, Kaushik Mallik, Satya Prakash Nayak +1
We solve the problem of automatically computing a new class of environment assumptions in two-player turn-based finite graph games which characterize an ``adequate cooperation'' ne…