mlsanityData to 7 Oct 2026

Trustworthy Social Bias Measurement

arXiv 2212.11672 ↗First posted 20 Dec 2022cs.CLcited by 7

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Abstract (arXiv)

How do we design measures of social bias that we trust? While prior work has introduced several measures, no measure has gained widespread trust: instead, mounting evidence argues we should distrust these measures. In this work, we design bias measures that warrant trust based on the cross-disciplinary theory of measurement modeling. To combat the frequently fuzzy treatment of social bias in NLP, we explicitly define social bias, grounded in principles drawn from social science research. We operationalize our definition by proposing a general bias measurement framework DivDist, which we use to instantiate 5 concrete bias measures. To validate our measures, we propose a rigorous testing protocol with 8 testing criteria (e.g. predictive validity: do measures predict biases in US employment?). Through our testing, we demonstrate considerable evidence to trust our measures, showing they overcome conceptual, technical, and empirical deficiencies present in prior measures.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 75 of its 134 references that are in the map. First posted on arXiv 20 Dec 2022.

Builds on

75 of 134 references.

References · 75

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38 more
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  18. 2021BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generationcited by 410
  19. 2019On Measuring Social Biases in Sentence Encoderscited by 397
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  22. 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Themcited by 268
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50 most cited of 75 All 75 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 2; 2026: 2.

7 papers.

  1. 2023Inseq: An Interpretability Toolkit for Sequence Generation Modelscited by 78
  2. 2025Toward an Evaluation Science for Generative AI Systemscited by 42
  3. 2024Identifying and Mitigating Social Bias Knowledge in Language Modelscited by 13
  4. 2025Understanding and Meeting Practitioner Needs When Measuring Representational Harms Caused by LLM-Based Systemscited by 3
  5. 2023Applying Standards to Advance Upstream & Downstream Ethics in Large Language Modelscited by 0
  6. 2026Design Theater: Evaluating the Gap Between User-Facing Design Reasoning and Implementation in Generative UI Toolscited by 0
  7. 2026Critical Acclaim Orientation in Large Language Models: Evidence from Film Preference Elicitationcited by 0