Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations

no code implementations • 14 Oct 2022 • Flavien Prost, Ben Packer, Jilin Chen, Li Wei, Pierre Kremp, Nicholas Blumm, Susan Wang, Tulsee Doshi, Tonia Osadebe, Lukasz Heldt, Ed H. Chi, Alex Beutel

We reconcile these notions and show that the tension is due to differences in distributions of users where items are relevant, and break down the important factors of the user's recommendations.

Fairness Recommendation Systems

Nova-Wang at SemEval-2020 Task 12: OffensEmblert: An Ensemble ofOffensive Language Classifiers

no code implementations • SEMEVAL 2020 • Susan Wang, Zita Marinho

Our best model, an average ensemble of four different Bert models, achieved 11th place out of 82 participants with a macro F1 score of 0. 91344 in the English SubTask A.

Classification regression