299 citations · 983 across the 34 of their papers we have counts for
5 papers · 1 filter
Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems
Xuezhi Wang, Nithum Thain, Anu Sinha +4
How can we build recommender systems to take into account fairness? Real-world recommender systems are often composed of multiple models, built by multiple teams. However, most res…
Toward a better trade-off between performance and fairness with kernel-based distribution matching
Flavien Prost, Hai Qian, Qiuwen Chen +3
As recent literature has demonstrated how classifiers often carry unintended biases toward some subgroups, deploying machine learned models to users demands careful consideration o…
Transfer of Machine Learning Fairness across Domains
Candice Schumann, Xuezhi Wang, Alex Beutel +3
If our models are used in new or unexpected cases, do we know if they will make fair predictions? Previously, researchers developed ways to debias a model for a single problem doma…
Fairness in Recommendation Ranking through Pairwise Comparisons
Alex Beutel, Jilin Chen, Tulsee Doshi +8
Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information. As such it…
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
Alex Beutel, Jilin Chen, Tulsee Doshi +6
As more researchers have become aware of and passionate about algorithmic fairness, there has been an explosion in papers laying out new metrics, suggesting algorithms to address i…