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Measuring Fairness with Biased Rulers: A Comparative Study on Bias Metrics for Pre-trained Language Models
Pieter Delobelle
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Ewoenam Tokpo
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Toon Calders
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Bettina Berendt
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Paper Details:
Month: July
Year: 2022
Location: Seattle, United States
Venue:
NAACL |
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http://www.fairmlbook.org
https://github.com/google-research-datasets/zari
https://github.com/keitakurita/contextual_embedding_bias_measure
https://github.com/marionbartl/gender-bias-BERT
https://github.com/W4ngatang/sent-bias
https://github.com/tanyichern/social-biases-contextualized
https://github.com/pliang279/LM_bias
https://github.com/facebookresearch/ParlAI/tree/main/parlai/tasks/md_gender
https://github.com/jsedoc/ConceptorDebias
https://github.com/sunipa/On-Measuring-and-Mitigating-Biased-Inferences-of-Word-Embeddings
https://github.com/moinnadeem/stereoset
https://github.com/nyu-mll/crows-pairs
https://github.com/rudinger/Winogender-schemas
https://github.com/uclanlp/corefBias
https://github.com/sebastianGehrmann/CausalMediationAnalysis
https://github.com/weiguowilliam/CEAT
https://github.com/MilaNLProc/honest
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