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Found 9 papers, 3 papers with code
Training individually fair ML models with Sensitive Subspace Robustness
We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs.
De-biasing "bias" measurement
When a model's performance differs across socially or culturally relevant groups--like race, gender, or the intersections of many such groups--it is often called "biased."
Preference Modeling with Context-Dependent Salient Features
Finally we demonstrate strong performance of maximum likelihood estimation of our model on both synthetic data and two real data sets: the UT Zappos50K data set and comparison data about the compactness of legislative districts in the US.
Fair Pipelines
This work facilitates ensuring fairness of machine learning in the real world by decoupling fairness considerations in compound decisions.
Preference modelling with context-dependent salient features
Finally we demonstrate the strong performance of maximum likelihood estimation of our model on both synthetic data and two real data sets: the UT Zappos50K data set and comparison data about the compactness of legislative districts in the United States.
Individually Fair Rankings
We develop an algorithm to train individually fair learning-to-rank (LTR) models.
Individually Fair Ranking
We develop an algorithm to train individually fair learning-to-rank (LTR) models.
Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics
We show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users.
Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems
Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user.
