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Found 5 papers, 4 papers with code
Superhuman Fairness
The fairness of machine learning-based decisions has become an increasingly important focus in the design of supervised machine learning methods.
Fairness for Robust Learning to Rank
While conventional ranking systems focus solely on maximizing the utility of the ranked items to users, fairness-aware ranking systems additionally try to balance the exposure for different protected attributes such as gender or race.
ParsiNLU: A Suite of Language Understanding Challenges for Persian
Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English.
Robust Fairness under Covariate Shift
We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same.
Fairness for Robust Log Loss Classification
Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications.
