8 citations · 27 across the 15 of their papers we have counts for
Showing 2022Show all
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cs.CL2022★ 2 cited
BLIND: Bias Removal With No Demographics
Hadas Orgad, Yonatan Belinkov
Models trained on real-world data tend to imitate and amplify social biases. Common methods to mitigate biases require prior information on the types of biases that should be mitig…
cs.CL2022
Choose Your Lenses: Flaws in Gender Bias Evaluation
Hadas Orgad, Yonatan Belinkov
Considerable efforts to measure and mitigate gender bias in recent years have led to the introduction of an abundance of tasks, datasets, and metrics used in this vein. In this pos…
cs.CL2022★ 2 cited
How Gender Debiasing Affects Internal Model Representations, and Why It Matters
Hadas Orgad, Seraphina Goldfarb-Tarrant, Yonatan Belinkov
Common studies of gender bias in NLP focus either on extrinsic bias measured by model performance on a downstream task or on intrinsic bias found in models' internal representation…