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Measuring and Reducing Gendered Correlations in Pre-trained Models
Pre-trained models have revolutionized natural language understanding. H...
10/12/2020 ∙ by Kellie Webster, et al. ∙
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CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
NLP models are shown to suffer from robustness issues, i.e., a model's p...
10/05/2020 ∙ by Tianlu Wang, et al. ∙
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Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems
Most literature in fairness has focused on improving fairness with respe...
11/05/2019 ∙ by Xuezhi Wang, et al. ∙
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Toward a better trade-off between performance and fairness with kernel-based distribution matching
As recent literature has demonstrated how classifiers often carry uninte...
10/25/2019 ∙ by Flavien Prost, et al. ∙
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Transfer of Machine Learning Fairness across Domains
If our models are used in new or unexpected cases, do we know if they wi...
06/24/2019 ∙ by Candice Schumann, et al. ∙
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Fairness in Recommendation Ranking through Pairwise Comparisons
Recommender systems are one of the most pervasive applications of machin...
03/02/2019 ∙ by Alex Beutel, et al. ∙
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Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
As more researchers have become aware of and passionate about algorithmi...
01/14/2019 ∙ by Alex Beutel, et al. ∙
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