mlsanityData to 7 Oct 2026

DebIE: A Platform for Implicit and Explicit Debiasing of Word Embedding Spaces

arXiv 2103.06598 ↗First posted 11 Mar 2021cs.CLcited by 5

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Abstract (arXiv)

Recent research efforts in NLP have demonstrated that distributional word vector spaces often encode stereotypical human biases, such as racism and sexism. With word representations ubiquitously used in NLP models and pipelines, this raises ethical issues and jeopardizes the fairness of language technologies. While there exists a large body of work on bias measures and debiasing methods, to date, there is no platform that would unify these research efforts and make bias measuring and debiasing of representation spaces widely accessible. In this work, we present DebIE, the first integrated platform for (1) measuring and (2) mitigating bias in word embeddings. Given an (i) embedding space (users can choose between the predefined spaces or upload their own) and (ii) a bias specification (users can choose between existing bias specifications or create their own), DebIE can (1) compute several measures of implicit and explicit bias and modify the embedding space by executing two (mutually composable) debiasing models. DebIE's functionality can be accessed through four different interfaces: (a) a web application, (b) a desktop application, (c) a REST-ful API, and (d) as a command-line application. DebIE is available at: debie.informatik.uni-mannheim.de.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 28 references that are in the map. First posted on arXiv 11 Mar 2021.

Builds on

21 of 28 references.

References · 21

  1. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  2. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  3. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  4. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  5. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 911
  6. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  7. 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Themcited by 268
  8. 2017Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypescited by 251
  9. 2020Measuring and Reducing Gendered Correlations in Pre-trained Modelscited by 222
  10. 2019Identifying and Reducing Gender Bias in Word-Level Language Modelscited by 214
  11. 2019Gender Bias in Contextualized Word Embeddingscited by 203
  12. 2014SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimationcited by 170
9 more
  1. 2019Queens are Powerful too: Mitigating Gender Bias in Dialogue Generationcited by 152
  2. 2017Offline bilingual word vectors, orthogonal transformations and the inverted softmaxcited by 94
  3. 2019Attenuating Bias in Word Vectorscited by 64
  4. 2019Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Functioncited by 57
  5. 2019How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptionscited by 48
  6. 2019A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spacescited by 36
  7. 2019Are We Consistently Biased? Multidimensional Analysis of Biases in Distributional Word Vectorscited by 31
  8. 2020AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddingscited by 20
  9. 2020Grammatical gender associations outweigh topical gender bias in crosslinguistic word embeddingscited by 9

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 1.

5 papers.

  1. 2023On the Independence of Association Bias and Empirical Fairness in Language Modelscited by 29
  2. 2024Fairness and Bias in Multimodal AI: A Surveycited by 19
  3. 2023BiasTestGPT: Using ChatGPT for Social Bias Testing of Language Modelscited by 8
  4. 2021VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representationscited by 3
  5. 2022AI Fairness: from Principles to Practicecited by 1