23 citations · 69 across the 19 of their papers we have counts for
4 papers · 1 filter
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…
Balancing out Bias: Achieving Fairness Through Balanced Training
Xudong Han, Timothy Baldwin, Trevor Cohn
Group bias in natural language processing tasks manifests as disparities in system error rates across texts authorized by different demographic groups, typically disadvantaging min…
Diverse Adversaries for Mitigating Bias in Training
Xudong Han, Timothy Baldwin, Trevor Cohn
Adversarial learning can learn fairer and less biased models of language than standard methods. However, current adversarial techniques only partially mitigate model bias, added to…