We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.
Sirui Yao (Virginia Polytechnic Institute and State University)
Bert Huang (Virginia Tech)
Bert Huang is an assistant professor in the Department of Computer Science at Virginia Tech, where he directs the Machine Learning Laboratory. He earned his Ph.D. from Columbia University in 2011, and spent three years as a postdoctoral research associate at the University of Maryland, College Park, before joining Virginia Tech in 2015. His research investigates machine learning, with a focus on analyzing complex systems. His work addresses topics including structured prediction, weakly supervised learning, and computational social science. His papers have been published at conferences including NeurIPS, ICML, UAI, and AISTATS, and he is an action editor for the Journal of Machine Learning Research.
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