9 citations · 20 across the 5 of their papers we have counts for
5 papers
From Models to Systems: A Comprehensive Fairness Framework for Compositional Recommender Systems
Brian Hsu, Cyrus DiCiccio, Natesh Sivasubramoniapillai +1
Fairness research in machine learning often centers on ensuring equitable performance of individual models. However, real-world recommendation systems are built on multiple models…
Disentangling and Operationalizing AI Fairness at LinkedIn
Joaquin Quiñonero-Candela, Yuwen Wu, Brian Hsu +5
Operationalizing AI fairness at LinkedIn's scale is challenging not only because there are multiple mutually incompatible definitions of fairness but also because determining what…
An Operational Perspective to Fairness Interventions: Where and How to Intervene
Brian Hsu, Xiaotong Chen, Ying Han +2
As AI-based decision systems proliferate, their successful operationalization requires balancing multiple desiderata: predictive performance, disparity across groups, safeguarding…
Pushing the limits of fairness impossibility: Who's the fairest of them all?
Brian Hsu, Rahul Mazumder, Preetam Nandy +1
The impossibility theorem of fairness is a foundational result in the algorithmic fairness literature. It states that outside of special cases, one cannot exactly and simultaneousl…
Detection and Mitigation of Algorithmic Bias via Predictive Rate Parity
Cyrus DiCiccio, Brian Hsu, YinYin Yu +2
Predictive parity (PP), also known as sufficiency, is a core definition of algorithmic fairness essentially stating that model outputs must have the same interpretation of expected…