35 citations · 94 across the 18 of their papers we have counts for
4 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…
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…
A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges
Usman Gohar, Lu Cheng
The widespread adoption of Machine Learning systems, especially in more decision-critical applications such as criminal sentencing and bank loans, has led to increased concerns abo…
Towards Understanding Fairness and its Composition in Ensemble Machine Learning
Usman Gohar, Sumon Biswas, Hridesh Rajan
Machine Learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc. Many recent works ha…