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Proceedings of the First Workshop on Gender Bias in Natural Language Processing · Study

WorkProceedings of the First Workshop on Gender Bias in Natural Language Processing

Unknown author2019

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Workgold2019

Proceedings of the First Workshop on Gender Bias in Natural Language Processing

Unknown author

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Ethics and Social Impacts of AI Natural Language Processing Techniques Gender Studies in Language

Abstract

has generated and we look forward to a lively discussion about how to tackle bias problems in NLP applications when we meet in Florence!

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References

1998Measuring individual differences in implicit cognition: The implicit association test.1998Measuring individual differences in implicit cognition: The implicit association test.1954Distributional Structure 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification 2014Big Data's Disparate Impact 2017Semantics derived automatically from language corpora contain human-like biases 2016Equality of Opportunity in Supervised Learning 1984Gender stereotypes stem from the distribution of women and men into social roles.

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2023Beyond rating scales: With targeted evaluation, large language models are poised for psychological assessment 2021How to measure gender bias in machine translation: Real-world oriented machine translators, multiple reference points

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Details

Year2019

Typeother

Published2019-01-01

Languageen

Citations42

Files0 public / 0

ShelfEthics and Social Impacts of AI

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doi10.18653/v1/w19-38

openalex_workW4252004167

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