35 citations · 94 across the 18 of their papers we have counts for
5 papers · 1 filter
Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML
Prakhar Ganesh, Usman Gohar, Lu Cheng +1
With fairness concerns gaining significant attention in Machine Learning (ML), several bias mitigation techniques have been proposed, often compared against each other to find the…
CoDefeater: Using LLMs To Find Defeaters in Assurance Cases
Usman Gohar, Michael C. Hunter, Robyn R. Lutz +1
Constructing assurance cases is a widely used, and sometimes required, process toward demonstrating that safety-critical systems will operate safely in their planned environment. T…
Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges
Usman Gohar, Zeyu Tang, Jialu Wang +4
The widespread integration of Machine Learning systems in daily life, particularly in high-stakes domains, has raised concerns about the fairness implications. While prior works ha…
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…
Towards Engineering Fair and Equitable Software Systems for Managing Low-Altitude Airspace Authorizations
Usman Gohar, Michael C. Hunter, Agnieszka Marczak-Czajka +3
Small Unmanned Aircraft Systems (sUAS) have gained widespread adoption across a diverse range of applications. This has introduced operational complexities within shared airspaces…