mlsanityData to 7 Oct 2026

Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models

arXiv 2307.09209 ↗First posted 18 Jul 2023cs.CL cs.AI cs.CYcited by 20

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Abstract (arXiv)

We analyze sentiment analysis and toxicity detection models to detect the presence of explicit bias against people with disability (PWD). We employ the bias identification framework of Perturbation Sensitivity Analysis to examine conversations related to PWD on social media platforms, specifically Twitter and Reddit, in order to gain insight into how disability bias is disseminated in real-world social settings. We then create the textitBias Identification Test in Sentiment (BITS) corpus to quantify explicit disability bias in any sentiment analysis and toxicity detection models. Our study utilizes BITS to uncover significant biases in four open AIaaS (AI as a Service) sentiment analysis tools, namely TextBlob, VADER, Google Cloud Natural Language API, DistilBERT and two toxicity detection models, namely two versions of Toxic-BERT. Our findings indicate that all of these models exhibit statistically significant explicit bias against PWD.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 20 of its 40 references that are in the map. First posted on arXiv 18 Jul 2023.

Builds on

20 of 40 references.

References · 20

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  3. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  4. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 911
  5. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  6. 2014Certifying and removing disparate impactcited by 715
  7. 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 703
  8. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 376
  9. 2019Measuring Bias in Contextualized Word Representationscited by 275
  10. 2018Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systemscited by 234
  11. 2020Social Biases in NLP Models as Barriers for Persons with Disabilitiescited by 182
  12. 2021Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologiescited by 128
8 more
  1. 2021On Releasing Annotator-Level Labels and Information in Datasetscited by 123
  2. 2020Toxicity Detection: Does Context Really Matter?cited by 93
  3. 2021Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metricscited by 83
  4. 2019Perturbation Sensitivity Analysis to Detect Unintended Model Biasescited by 74
  5. 2023Nationality Bias in Text Generationcited by 50
  6. 2022Using Natural Sentences for Understanding Biases in Language Modelscited by 19
  7. 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"cited by 15
  8. 2018Machine Learning Suites for Online Toxicity Detectioncited by 12

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Citing papers per year: 2023: 2; 2024: 7; 2025: 6; 2026: 5.

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  1. 2024Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approachcited by 25
  2. 2025Fairness through Difference Awareness: Measuring Desired Group Discrimination in LLMscited by 20
  3. 2024Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responsescited by 15
  4. 2023CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Biascited by 13
  5. 2024From Melting Pots to Misrepresentations: Exploring Harms in Generative AIcited by 13
  6. 2023The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysiscited by 12
  7. 2025Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variationscited by 12
  8. 2025AccessEval: Benchmarking Disability Bias in Large Language Modelscited by 5
  9. 2024Disability Representations: Finding Biases in Automatic Image Generationcited by 4
  10. 2024ASTPrompter: Preference-Aligned Automated Language Model Red-Teaming to Generate Low-Perplexity Unsafe Promptscited by 3
  11. 2024AUTALIC: A Dataset for Anti-AUTistic Ableist Language In Contextcited by 3
  12. 2024Race and Privacy in Broadcast Police Communicationscited by 1
8 more
  1. 2025"Accessibility people, you go work on that thing of yours over there": Addressing Disability Inclusion in AI Product Organizationscited by 1
  2. 2025Navigating the Rabbit Hole: Emergent Biases in LLM-Generated Attack Narratives Targeting Mental Health Groupscited by 0
  3. 2025Who Gets Left Behind? Auditing Disability Inclusivity in Large Language Modelscited by 0
  4. 2026Investigating Disability Representations in Text-to-Image Modelscited by 0
  5. 2026"I followed what felt right, not what I was told": Autonomy, Coaching, and Recognizing Bias Through AI-Mediated Dialoguecited by 0
  6. 2026DisaBench: A Participatory Evaluation Framework for Disability Harms in Language Modelscited by 0
  7. 2026Beyond wheelchairs and blindfolds: Investigating disability stereotypes in T2I models with INCLUDE-BENCHcited by 0
  8. 2026From Interpretability to Control: Insights from Six Years of the TrustNLP Workshopcited by 0