35 citations · 154 across the 16 of their papers we have counts for
8 papers · 1 filter
FAIRER: Fairness as Decision Rationale Alignment
Tianlin Li, Qing Guo, Aishan Liu +3
Deep neural networks (DNNs) have made significant progress, but often suffer from fairness issues, as deep models typically show distinct accuracy differences among certain subgrou…
Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors
Zeyu Tang, Yatong Chen, Yang Liu +1
The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process. In this paper, through causal modeling with a directed…
Understanding Instance-Level Impact of Fairness Constraints
Jialu Wang, Xin Eric Wang, Yang Liu
A variety of fairness constraints have been proposed in the literature to mitigate group-level statistical bias. Their impacts have been largely evaluated for different groups of p…
Metric-Fair Classifier Derandomization
Jimmy Wu, Yatong Chen, Yang Liu
We study the problem of classifier derandomization in machine learning: given a stochastic binary classifier , sample a deterministic classifier $\hat{f}: X \to \{0…
Fairness Transferability Subject to Bounded Distribution Shift
Yatong Chen, Reilly Raab, Jialu Wang +1
Given an algorithmic predictor that is "fair" on some source distribution, will it still be fair on an unknown target distribution that differs from the source within some bound? I…
Adaptive Data Debiasing through Bounded Exploration
Yifan Yang, Yang Liu, Parinaz Naghizadeh
Biases in existing datasets used to train algorithmic decision rules can raise ethical and economic concerns due to the resulting disparate treatment of different groups. We propos…
Model Transferability With Responsive Decision Subjects
Yatong Chen, Zeyu Tang, Kun Zhang +1
Given an algorithmic predictor that is accurate on some source population consisting of strategic human decision subjects, will it remain accurate if the population respond to it?…
Fair Classification with Group-Dependent Label Noise
Jialu Wang, Yang Liu, Caleb Levy
This work examines how to train fair classifiers in settings where training labels are corrupted with random noise, and where the error rates of corruption depend both on the label…