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07/11/2023
Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification
Despite the rich literature on machine learning fairness, relatively lit...
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06/25/2023
Let's Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning
Language models still struggle on moral reasoning, despite their impress...
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03/01/2023
A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures
Despite huge gains in performance in natural language understanding via ...
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10/28/2022
Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers
Large pre-trained language models have shown remarkable performance over...
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10/18/2022
A Human-ML Collaboration Framework for Improving Video Content Reviews
We deal with the problem of localized in-video taxonomic human annotatio...
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06/28/2022
Flexible text generation for counterfactual fairness probing
A common approach for testing fairness issues in text-based classifiers ...
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10/12/2020
Measuring and Reducing Gendered Correlations in Pre-trained Models
Pre-trained models have revolutionized natural language understanding. H...
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10/05/2020
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...
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11/05/2019
Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems
Most literature in fairness has focused on improving fairness with respe...
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10/25/2019
Toward a better trade-off between performance and fairness with kernel-based distribution matching
As recent literature has demonstrated how classifiers often carry uninte...
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06/24/2019
Transfer of Machine Learning Fairness across Domains
If our models are used in new or unexpected cases, do we know if they wi...
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03/02/2019
Fairness in Recommendation Ranking through Pairwise Comparisons
Recommender systems are one of the most pervasive applications of machin...
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01/14/2019
