40 citations · 100 across the 68 of their papers we have counts for
5 papers · 1 filter
Beyond Detection: Unveiling Fairness Vulnerabilities in Abusive Language Models
Yueqing Liang, Lu Cheng, Ali Payani +1
This work investigates the potential of undermining both fairness and detection performance in abusive language detection. In a dynamic and complex digital world, it is crucial to…
Interpreting Pretrained Language Models via Concept Bottlenecks
Zhen Tan, Lu Cheng, Song Wang +3
Pretrained language models (PLMs) have made significant strides in various natural language processing tasks. However, the lack of interpretability due to their ``black-box'' natur…
Equal Opportunity of Coverage in Fair Regression
Fangxin Wang, Lu Cheng, Ruocheng Guo +2
We study fair machine learning (ML) under predictive uncertainty to enable reliable and trustworthy decision-making. The seminal work of ``equalized coverage'' proposed an uncertai…
A Theoretical Approach to Characterize the Accuracy-Fairness Trade-off Pareto Frontier
Hua Tang, Lu Cheng, Ninghao Liu +1
While the accuracy-fairness trade-off has been frequently observed in the literature of fair machine learning, rigorous theoretical analyses have been scarce. To demystify this lon…
Fair Few-shot Learning with Auxiliary Sets
Song Wang, Jing Ma, Lu Cheng +1
Recently, there has been a growing interest in developing machine learning (ML) models that can promote fairness, i.e., eliminating biased predictions towards certain populations (…