Queer NLP: A Critical Survey on Literature Gaps, Biases and Trends

2026-02-17

AI Summary

This study addresses the systemic biases in natural language processing (NLP) technologies that disproportionately affect marginalized groups, particularly LGBTQIA+ communities, an issue insufficiently examined in existing literature. For the first time, it offers a systematic review of all NLP research explicitly engaging with LGBTQIA+ topics in the ACL anthology, drawing on queer theory and employing qualitative content analysis within a critical theoretical framework. The analysis reveals that current approaches predominantly focus on post-hoc bias detection rather than proactive inclusive design. In response, this work proposes a forward-looking agenda emphasizing meaningful stakeholder engagement, intersectional analysis, exploration of non-English linguistic contexts, and deeper interdisciplinary collaboration. These recommendations aim to chart a pathway toward more equitable and inclusive NLP systems.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Natural language processing (NLP) technologies are rapidly reshaping how language is created, processed, and analyzed by humans. With current and potential applications in hiring, law, healthcare, and other areas that impact people's lives, understanding and mitigating harms towards marginalized groups is critical. In this survey, we examine NLP research papers that explicitly address the relationship between LGBTQIA+ communities and NLP technologies. We systematically review all such papers published in the ACL Anthology, to answer the following research questions: (1) What are current research trends? (2) What gaps exist in terms of topics and methods? (3) What areas are open for future work? We find that while the number of papers on queer NLP has grown within the last few years, most papers take a reactive rather than a proactive approach, pointing out bias more often than mitigating it, and focusing on shortcomings of existing systems rather than creating new solutions. Our survey uncovers many opportunities for future work, especially regarding stakeholder involvement, intersectionality, interdisciplinarity, and languages other than English. We also offer an outlook from a queer studies perspective, highlighting understudied topics and gaps in the harms addressed in NLP papers. Beyond being a roadmap of what has been done, this survey is a call to action for work towards more just and inclusive NLP technologies.

Problem

• Queer NLP
• LGBTQIA+
• bias in NLP
• marginalized groups
• inclusive NLP

Innovation

• Queer NLP
• bias mitigation
• intersectionality
• stakeholder involvement
• inclusive AI