26 citations · 55 across the 10 of their papers we have counts for
6 papers · 1 filter
Adapting Fairness Interventions to Missing Values
Raymond Feng, Flavio P. Calmon, Hao Wang
Missing values in real-world data pose a significant and unique challenge to algorithmic fairness. Different demographic groups may be unequally affected by missing data, and the s…
Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
Hao Wang, Luxi He, Rui Gao +1
Machine learning (ML) models can underperform on certain population groups due to choices made during model development and bias inherent in the data. We categorize sources of disc…
Beyond Adult and COMPAS: Fairness in Multi-Class Prediction
Wael Alghamdi, Hsiang Hsu, Haewon Jeong +4
We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks. We formulate this problem in terms of "projecting" a pre-trained (and pote…
Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values
Haewon Jeong, Hao Wang, Flavio P. Calmon
We investigate the fairness concerns of training a machine learning model using data with missing values. Even though there are a number of fairness intervention methods in the lit…
To Split or Not to Split: The Impact of Disparate Treatment in Classification
Hao Wang, Hsiang Hsu, Mario Diaz +1
Disparate treatment occurs when a machine learning model yields different decisions for individuals based on a sensitive attribute (e.g., age, sex). In domains where prediction acc…
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
Hao Wang, Berk Ustun, Flavio P. Calmon
When the performance of a machine learning model varies over groups defined by sensitive attributes (e.g., gender or ethnicity), the performance disparity can be expressed in terms…