Runtime monitoring of dynamic fairness properties
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 …
societal impacts in the long-run. This may happen when the system interacts with humans …
Monitoring unmanned aircraft: specification, integration, and lessons-learned
This paper reports on the integration of runtime monitoring into fully-electric aircraft designed
by Volocopter, a German aircraft manufacturer of electric multi-rotor helicopters. The runtime …
by Volocopter, a German aircraft manufacturer of electric multi-rotor helicopters. The runtime …
Monitoring algorithmic fairness under partial observations
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 …
humans, it is imperative that they remain fair and unbiased in their decisions. To …
Counterfactual Fairness With the Human in the Loop
Machine learning models have been increasingly used in human-related applications such
as healthcare, lending, and college admissions. As a result, there are growing concerns …
as healthcare, lending, and college admissions. As a result, there are growing concerns …