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09/12/2022
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 ...
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05/11/2022
De-biasing "bias" measurement
When a model's performance differs across socially or culturally relevan...
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02/03/2022
Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics
The harmful impacts of algorithmic decision systems have recently come i...
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03/19/2021
Individually Fair Ranking
We develop an algorithm to train individually fair learning-to-rank (LTR...
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02/22/2020
Preference Modeling with Context-Dependent Salient Features
We consider the problem of estimating a ranking on a set of items from n...
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06/28/2019
Learning fair predictors with Sensitive Subspace Robustness
We consider an approach to training machine learning systems that are fa...
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07/02/2018
Debiasing representations by removing unwanted variation due to protected attributes
We propose a regression-based approach to removing implicit biases in re...
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07/03/2017
