@inproceedings{queer-in-ai-etal-2026-queering,
title = "Queering the Audits: Community-Based Auditing of {AI} Harms to Queer Communities",
author = "Queer In AI, Organizers of and
Pranav, A and
Valentine, Alissa A. and
Markham, Alex and
LeClair, Beckett and
Blazkova, Tereza and
Kornilitsina, Ekaterina and
Bruun, Sofie H. and
Spanakis, Gerasimos and
Lauscher, Anne",
editor = "Pranav, A and
Basile, Valerio and
Falk, Neele and
Jurgens, David and
Lapesa, Gabriella and
Lauscher, Anne and
Lo, Soda Marem",
booktitle = "Proceedings of the Second Workshop of Identity Aware {AI}",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2026.iaai-1.6/",
doi = "10.63317/5acrwqmesyw4",
pages = "66--76",
abstract = "AI systems embed majority-group defaults into training data, evaluation metrics, and category definitions, producing documented harms for queer communities including erasure, misclassification, and discrimination. Standard technical audits often rely on aggregate measures and cannot detect harms that be come visible only through the lived experience of affected communities. We conducted a participatory auditing workshop at EurIPS 2025 where 16 queer community members audited four case studies using the 4Cs harm taxonomy (Content, Conduct, Contact, Contract) applied across the AI lifecycle. Participants used structured worksheets and plenary synthesis to classify harms and trace them to their origins in the development pipeline. Across all four cases, participants traced harms to problem definition and data collection, and they identified contractual structures that extract value from vulnerable populations while providing minimal recourse. These findings illustrate that community-informed auditing surfaces concrete, identity-specific harms that aggregate evaluation methods risk overlooking."
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<abstract>AI systems embed majority-group defaults into training data, evaluation metrics, and category definitions, producing documented harms for queer communities including erasure, misclassification, and discrimination. Standard technical audits often rely on aggregate measures and cannot detect harms that be come visible only through the lived experience of affected communities. We conducted a participatory auditing workshop at EurIPS 2025 where 16 queer community members audited four case studies using the 4Cs harm taxonomy (Content, Conduct, Contact, Contract) applied across the AI lifecycle. Participants used structured worksheets and plenary synthesis to classify harms and trace them to their origins in the development pipeline. Across all four cases, participants traced harms to problem definition and data collection, and they identified contractual structures that extract value from vulnerable populations while providing minimal recourse. These findings illustrate that community-informed auditing surfaces concrete, identity-specific harms that aggregate evaluation methods risk overlooking.</abstract>
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%0 Conference Proceedings
%T Queering the Audits: Community-Based Auditing of AI Harms to Queer Communities
%A Queer In AI, Organizers of
%A Pranav, A.
%A Valentine, Alissa A.
%A Markham, Alex
%A LeClair, Beckett
%A Blazkova, Tereza
%A Kornilitsina, Ekaterina
%A Bruun, Sofie H.
%A Spanakis, Gerasimos
%A Lauscher, Anne
%Y Pranav, A.
%Y Basile, Valerio
%Y Falk, Neele
%Y Jurgens, David
%Y Lapesa, Gabriella
%Y Lauscher, Anne
%Y Lo, Soda Marem
%S Proceedings of the Second Workshop of Identity Aware AI
%D 2026
%8 May
%I European Language Resources Association
%C Palma de Mallorca, Spain
%F queer-in-ai-etal-2026-queering
%X AI systems embed majority-group defaults into training data, evaluation metrics, and category definitions, producing documented harms for queer communities including erasure, misclassification, and discrimination. Standard technical audits often rely on aggregate measures and cannot detect harms that be come visible only through the lived experience of affected communities. We conducted a participatory auditing workshop at EurIPS 2025 where 16 queer community members audited four case studies using the 4Cs harm taxonomy (Content, Conduct, Contact, Contract) applied across the AI lifecycle. Participants used structured worksheets and plenary synthesis to classify harms and trace them to their origins in the development pipeline. Across all four cases, participants traced harms to problem definition and data collection, and they identified contractual structures that extract value from vulnerable populations while providing minimal recourse. These findings illustrate that community-informed auditing surfaces concrete, identity-specific harms that aggregate evaluation methods risk overlooking.
%R 10.63317/5acrwqmesyw4
%U https://aclanthology.org/2026.iaai-1.6/
%U https://doi.org/10.63317/5acrwqmesyw4
%P 66-76
Markdown (Informal)
[Queering the Audits: Community-Based Auditing of AI Harms to Queer Communities](https://aclanthology.org/2026.iaai-1.6/) (Queer In AI et al., iaai 2026)
ACL
- Organizers of Queer In AI, A Pranav, Alissa A. Valentine, Alex Markham, Beckett LeClair, Tereza Blazkova, Ekaterina Kornilitsina, Sofie H. Bruun, Gerasimos Spanakis, and Anne Lauscher. 2026. Queering the Audits: Community-Based Auditing of AI Harms to Queer Communities. In Proceedings of the Second Workshop of Identity Aware AI, pages 66–76, Palma de Mallorca, Spain. European Language Resources Association.