Papers by Aurélien Bellet
Fair NLP Models with Differentially Private Text Encoders (2022.findings-emnlp)
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| Challenge: | Encoded text representations often capture sensitive attributes about individuals, raising privacy concerns and making models unfair to certain groups. |
| Approach: | They propose an approach that combines privacy and adversarial training to learn private representations which induces fairer models. |
| Outcome: | The proposed approach improves on four NLP datasets and shows that privacy and fairness can positively reinforce each other. |
Fair Without Leveling Down: A New Intersectional Fairness Definition (2023.emnlp-main)
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| Challenge: | Existing approaches to capture intersectional group fairness lack significant unfairness at intersection levels. |
| Approach: | They propose a new definition of intersectional fairness that combines absolute and relative performance across sensitive groups. |
| Outcome: | The proposed definition does not improve on a simple baseline. |