19 citations · 33 across the 6 of their papers we have counts for
8 papers
Systematic Evaluation of Predictive Fairness
Xudong Han, Aili Shen, Trevor Cohn +2
Mitigating bias in training on biased datasets is an important open problem. Several techniques have been proposed, however the typical evaluation regime is very limited, consideri…
Optimising Equal Opportunity Fairness in Model Training
Aili Shen, Xudong Han, Trevor Cohn +2
Real-world datasets often encode stereotypes and societal biases. Such biases can be implicitly captured by trained models, leading to biased predictions and exacerbating existing…
fairlib: A Unified Framework for Assessing and Improving Classification Fairness
Xudong Han, Aili Shen, Yitong Li +3
This paper presents fairlib, an open-source framework for assessing and improving classification fairness. It provides a systematic framework for quickly reproducing existing basel…
Towards Equal Opportunity Fairness through Adversarial Learning
Xudong Han, Timothy Baldwin, Trevor Cohn
Adversarial training is a common approach for bias mitigation in natural language processing. Although most work on debiasing is motivated by equal opportunity, it is not explicitl…
Contrastive Learning for Fair Representations
Aili Shen, Xudong Han, Trevor Cohn +2
Trained classification models can unintentionally lead to biased representations and predictions, which can reinforce societal preconceptions and stereotypes. Existing debiasing me…
Evaluating Debiasing Techniques for Intersectional Biases
Shivashankar Subramanian, Xudong Han, Timothy Baldwin +2
Bias is pervasive in NLP models, motivating the development of automatic debiasing techniques. Evaluation of NLP debiasing methods has largely been limited to binary attributes in…