# Queer In AI: A Case Study in Community-Led Participatory AI

arXiv: https://arxiv.org/abs/2303.16972 · HTML: https://mlsanity.com/paper/2303.16972/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 149 references that are in the map.
- First posted on arXiv 29 Mar 2023.

## Builds on (most cited first, 21 of 21)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus](https://mlsanity.com/paper/2104.08758/) (2021)
- [Multimodal datasets: misogyny, pornography, and malignant stereotypes](https://mlsanity.com/paper/2110.01963/) (2021)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies](https://mlsanity.com/paper/2108.12084/) (2021)
- [Towards a Critical Race Methodology in Algorithmic Fairness](https://mlsanity.com/paper/1912.03593/) (2019)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [Toward Gender-Inclusive Coreference Resolution](https://mlsanity.com/paper/1910.13913/) (2019)
- [Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities](https://mlsanity.com/paper/2102.04257/) (2021)
- [Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research](https://mlsanity.com/paper/2102.01203/) (2021)
- [Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches](https://mlsanity.com/paper/2205.04460/) (2022)
- [Subverting machines, fluctuating identities: Re-learning human categorization](https://mlsanity.com/paper/2205.13740/) (2022)
- [Rebuilding Trust: Queer in AI Approach to Artificial Intelligence Risk Management](https://mlsanity.com/paper/2110.09271/) (2021)
- [LGBTQ Privacy Concerns on Social Media](https://mlsanity.com/paper/2112.00107/) (2021)

## Built on by (most cited first, 30 of 34)

- [Collective Constitutional AI: Aligning a Language Model with Public Input](https://mlsanity.com/paper/2406.07814/) (2024)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [(A)I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice](https://mlsanity.com/paper/2402.01864/) (2024)
- [Participation in the age of foundation models](https://mlsanity.com/paper/2405.19479/) (2024)
- [A Robot Walks into a Bar: Can Language Models Serve as Creativity Support Tools for Comedy? An Evaluation of LLMs' Humour Alignment with Comedians](https://mlsanity.com/paper/2405.20956/) (2024)
- [Farsight: Fostering Responsible AI Awareness During AI Application Prototyping](https://mlsanity.com/paper/2402.15350/) (2024)
- [LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actions](https://mlsanity.com/paper/2406.08824/) (2024)
- [Akal Badi ya Bias: An Exploratory Study of Gender Bias in Hindi Language Technology](https://mlsanity.com/paper/2405.06346/) (2024)
- [Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You](https://mlsanity.com/paper/2401.16092/) (2024)
- [Scaling Laws Do Not Scale](https://mlsanity.com/paper/2307.03201/) (2023)
- [Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms](https://mlsanity.com/paper/2307.10223/) (2023)
- [The Impact and Opportunities of Generative AI in Fact-Checking](https://mlsanity.com/paper/2405.15985/) (2024)
- [Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits](https://mlsanity.com/paper/2403.14791/) (2024)
- [GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models](https://mlsanity.com/paper/2408.12494/) (2024)
- [Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Models](https://mlsanity.com/paper/2503.09805/) (2025)
- [The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models](https://mlsanity.com/paper/2411.03700/) (2024)
- [Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance](https://mlsanity.com/paper/2408.01458/) (2024)
- [LLMs Reproduce Stereotypes of Sexual and Gender Minorities](https://mlsanity.com/paper/2501.05926/) (2025)
- [Sound Check: Auditing Audio Datasets](https://mlsanity.com/paper/2410.13114/) (2024)
- [The Right to AI](https://mlsanity.com/paper/2501.17899/) (2025)
- [Secondary Stakeholders in AI: Fighting for, Brokering, and Navigating Agency](https://mlsanity.com/paper/2506.07281/) (2025)
- [Data Defenses Against Large Language Models](https://mlsanity.com/paper/2410.13138/) (2024)
- [The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and its Implications for Participation](https://mlsanity.com/paper/2502.03038/) (2025)
- [Gender Trouble in Language Models: An Empirical Audit Guided by Gender Performativity Theory](https://mlsanity.com/paper/2505.14080/) (2025)
- [MDIT-Bench: Evaluating the Dual-Implicit Toxicity in Large Multimodal Models](https://mlsanity.com/paper/2505.17144/) (2025)
- [The Role of Generative AI in Facilitating Social Interactions: A Scoping Review](https://mlsanity.com/paper/2506.10927/) (2025)
- [ModelCitizens: Representing Community Voices in Online Safety](https://mlsanity.com/paper/2507.05455/) (2025)
- [Weisfeiler and Leman Go Measurement Modeling: Probing the Validity of the WL Test](https://mlsanity.com/paper/2307.05775/) (2023)
- [Should they? Mobile Biometrics and Technopolicy meet Queer Community Considerations](https://mlsanity.com/paper/2311.10725/) (2023)
- [A Survey on Stereotype Detection in Natural Language Processing](https://mlsanity.com/paper/2505.17642/) (2025)

## Abstract (arXiv)

We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community's programs over the years. We discuss different challenges that emerged in the process, look at ways this organization has fallen short of operationalizing participatory and intersectional principles, and then assess the organization's impact. Queer in AI provides important lessons and insights for practitioners and theorists of participatory methods broadly through its rejection of hierarchy in favor of decentralization, success at building aid and programs by and for the queer community, and effort to change actors and institutions outside of the queer community. Finally, we theorize how communities like Queer in AI contribute to the participatory design in AI more broadly by fostering cultures of participation in AI, welcoming and empowering marginalized participants, critiquing poor or exploitative participatory practices, and bringing participation to institutions outside of individual research projects. Queer in AI's work serves as a case study of grassroots activism and participatory methods within AI, demonstrating the potential of community-led participatory methods and intersectional praxis, while also providing challenges, case studies, and nuanced insights to researchers developing and using participatory methods.