208 citations · 222 across the 4 of their papers we have counts for
5 papers
On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations
Yang Trista Cao, Yada Pruksachatkun, Kai-Wei Chang +4
Multiple metrics have been introduced to measure fairness in various natural language processing tasks. These metrics can be roughly categorized into two categories: 1) \emph{extri…
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal
Umang Gupta, Jwala Dhamala, Varun Kumar +7
Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings. However,…
Measuring Fairness of Text Classifiers via Prediction Sensitivity
Satyapriya Krishna, Rahul Gupta, Apurv Verma +3
With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions. Although various fairness definitions have b…
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification
Yada Pruksachatkun, Satyapriya Krishna, Jwala Dhamala +2
Existing bias mitigation methods to reduce disparities in model outcomes across cohorts have focused on data augmentation, debiasing model embeddings, or adding fairness-based opti…
BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation
Jwala Dhamala, Tony Sun, Varun Kumar +4
Recent advances in deep learning techniques have enabled machines to generate cohesive open-ended text when prompted with a sequence of words as context. While these models now emp…