Papers by John Lalor
Benchmarking Intersectional Biases in NLP (2022.naacl-main)
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| Challenge: | Recent work on fairness of machine learning models has focused on how to debias, but research on the fairness and performance of biased/debiased models on downstream prediction tasks has been limited. |
| Approach: | They assess intersectional bias - fairness across multiple demographic dimensions . they highlight possible causes and make recommendations for future NLP debiasing research. |
| Outcome: | The proposed approaches fare well in terms of fairness-accuracy trade-off, but are unable to effectively alleviate bias in downstream tasks. |