51 citations · 161 across the 10 of their papers we have counts for
11 papers
Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems
Amanda Bower, Kristian Lum, Tomo Lazovich +2
Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user. In recent years, methods relying on stochastic o…
De-biasing "bias" measurement
Kristian Lum, Yunfeng Zhang, Amanda Bower
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." While mu…
Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics
Tomo Lazovich, Luca Belli, Aaron Gonzales +5
The harmful impacts of algorithmic decision systems have recently come into focus, with many examples of systems such as machine learning (ML) models amplifying existing societal b…
Individually Fair Ranking
Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin +1
We develop an algorithm to train individually fair learning-to-rank (LTR) models. The proposed approach ensures items from minority groups appear alongside similar items from major…
Preference Modeling with Context-Dependent Salient Features
Amanda Bower, Laura Balzano
We consider the problem of estimating a ranking on a set of items from noisy pairwise comparisons given item features. We address the fact that pairwise comparison data often refle…
Training individually fair ML models with Sensitive Subspace Robustness
Mikhail Yurochkin, Amanda Bower, Yuekai Sun
We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs. For example, the pe…