238 citations · 642 across the 25 of their papers we have counts for
4 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…
Debiasing Embeddings for Reduced Gender Bias in Text Classification
Flavien Prost, Nithum Thain, Tolga Bolukbasi
(Bolukbasi et al., 2016) demonstrated that pretrained word embeddings can inherit gender bias from the data they were trained on. We investigate how this bias affects downstream cl…
Limitations of Pinned AUC for Measuring Unintended Bias
Daniel Borkan, Lucas Dixon, John Li +3
This report examines the Pinned AUC metric introduced and highlights some of its limitations. Pinned AUC provides a threshold-agnostic measure of unintended bias in a classificatio…
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
Daniel Borkan, Lucas Dixon, Jeffrey Sorensen +2
Unintended bias in Machine Learning can manifest as systemic differences in performance for different demographic groups, potentially compounding existing challenges to fairness in…