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Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English
6 references·16 citing·46 second hop
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this work references cited by second hop

References

  • 1975Language in the inner city: studies in the Black English Vernacular. By William Labov. Philadelphia: University of Pennsylvania Press, 1972. Pp. xxiv, 412. $6.95.3,019 cited
  • 2015Certifying and Removing Disparate Impact1,827 cited
  • 2004African American English: A Linguistic Introduction406 cited
  • 1999African American Vernacular English: Features, Evolution, Educational Implications350 cited
  • 2016The Social Impact of Natural Language Processing329 cited
  • 2015Challenges of studying and processing dialects in social media62 cited

Cited by

  • 2021More than a biomarker: could language be a biosocial marker of psychosis?74 cited
  • 2021Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics72 cited
  • 2019A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings58 cited
  • 2021Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement50 cited
  • 2019Validity Concerns in Research Using Organic Data50 cited
  • 2022Upstream Mitigation Is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language Models42 cited
  • 2023Fairness in Language Models Beyond English: Gaps and Challenges37 cited
  • 2020Technological Innovation and Discrimination in Household Finance

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36 cited
  • 2023Fair Enough: Searching for Sufficient Measures of Fairness34 cited
  • 2023Evaluation of African American Language Bias in Natural Language Generation23 cited
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  • 2019Bayesian Fairness20 cited
  • 2021Advancing social justice through linguistic justice: Strategies for building equity fluent NLP technology20 cited
  • 2024Learning to Be Fair: A Consequentialist Approach to Equitable Decision Making19 cited
  • 2022Signs Taken for Wonders: AI, Art & the Matter of Race18 cited
  • 2023Transcending the “Male Code”: Implicit Masculine Biases in NLP Contexts16 cited