Taxonomizing Representational Harms using Speech Act Theory
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Abstract (arXiv)
Representational harms are widely recognized among fairness-related harms caused by generative language systems. However, their definitions are commonly under-specified. We make a theoretical contribution to the specification of representational harms by introducing a framework, grounded in speech act theory (Austin, 1962), that conceptualizes representational harms caused by generative language systems as the perlocutionary effects (i.e., real-world impacts) of particular types of illocutionary acts (i.e., system behaviors). Building on this argument and drawing on relevant literature from linguistic anthropology and sociolinguistics, we provide new definitions of stereotyping, demeaning, and erasure. We then use our framework to develop a granular taxonomy of illocutionary acts that cause representational harms, going beyond the high-level taxonomies presented in previous work. We also discuss the ways that our framework and taxonomy can support the development of valid measurement instruments. Finally, we demonstrate the utility of our framework and taxonomy via a case study that engages with recent conceptual debates about what constitutes a representational harm and how such harms should be measured.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 20 of its 114 references that are in the map. First posted on arXiv 1 Apr 2025.
Builds on
20 of 114 references.
References · 20
- 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 956
- 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 924
- 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 710
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- 2022ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detectioncited by 422
- 2019Social Bias Frames: Reasoning about Social and Power Implications of Languagecited by 380
- 2022Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reductioncited by 138
- 2017Understanding Abuse: A Typology of Abusive Language Detection Subtaskscited by 116
- 201850 Years of Test (Un)fairness: Lessons for Machine Learningcited by 101
- 2023Evaluating the Social Impact of Generative AI Systems in Systems and Societycited by 92
- 2021On Measures of Biases and Harms in NLPcited by 81
- 2025Position: Evaluating Generative AI Systems Is a Social Science Measurement Challengecited by 63
8 more
- 2022Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluationcited by 59
- 2022Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Modelscited by 49
- 2024AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policiescited by 37
- 2023Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairnesscited by 26
- 2023Taxonomizing and Measuring Representational Harms: A Look at Image Taggingcited by 25
- 2024A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harmscited by 19
- 2024Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech Generatorscited by 18
- 2024Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigationcited by 17
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Citing papers per year: 2025: 3; 2026: 4.
7 papers.
- 2025Taxonomy of User Needs and Actionscited by 15
- 2025Reasoning Beyond Labels: Measuring LLM Sentiment in Low-Resource, Culturally Nuanced Contextscited by 2
- 2026Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global Southcited by 1
- 2026Same question, different history: language, national identity, and credit in large language modelscited by 1
- 2025Exposure to Content Written by Large Language Models Can Reduce Stigma Around Opioid Use Disorder in Online Communitiescited by 0
- 2026Representation Fidelity:Auditing Algorithmic Decisions About Humans Using Self-Descriptionscited by 0
- 2026On Defining Erasure Harms for NLPcited by 0
- 2026Same question, different history: language, national identity, and credit in large language models22 Jun 2026
- 2026On Defining Erasure Harms for NLP14 Jun 2026
- 2026Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global South18 May 2026
- 2026Representation Fidelity:Auditing Algorithmic Decisions About Humans Using Self-Descriptions5 Mar 2026
- 2025Taxonomy of User Needs and Actions7 Oct 2025
- 2025Reasoning Beyond Labels: Measuring LLM Sentiment in Low-Resource, Culturally Nuanced Contexts6 Aug 2025
- 2025Exposure to Content Written by Large Language Models Can Reduce Stigma Around Opioid Use Disorder in Online Communities8 Apr 2025