Third and Last Call for Papers: The 7th Workshop on Gender Bias in NLP (GeBNLP 2026) 5th AACL & 15th IJCNLP, November 9, 2026 Hengqin, China (Remote-only mode) About the Workshop The GeBNLP workshop https://gebnlp-workshop.github.io/ serves as a leading venue for the study, evaluation, and mitigation of gender bias in natural language processing. As large language models (LLMs) become foundational to recent NLP applications, addressing how these systems represent and affect different genders, alongside intersecting demographic axes such as race, ethnicity, nationality, religion, and ability, remains a critical challenge for the AI community. While foundational progress has been made in algorithmic debiasing and balanced data collection, recent large-scale evaluations reveal that state-of-the-art models continue to exhibit persistent stereotypes and confidence disparities across intersectional identities (Siddique et al., 2024; Savoldi et al., 2024). Our workshop serves as a multidisciplinary bridge, enabling researchers to define shared standards for tasks and metrics that ensure technical advancements are deeply rooted in the social and ethical realities of systemic harm (Dai et al., 2024).
Topics of Interest We invite submissions on a wide range of topics related to gender bias in NLP, including but not limited to:
Measurement and Evaluation: New metrics and datasets for quantifying bias in LLMs, MT, and multimodal systems. Mitigation Strategies: Technical approaches to debiasing (e.g., fine-tuning, adapter-based methods, or prompting strategies). Multilingual and Cross-Cultural Perspectives: Bias in low-resource languages or non-Western cultural contexts. Intersectionality: Research exploring how gender bias intersects with race, disability, age, or nationality. Ethical and Legal Frameworks: Policy implications and the "Human-in-the-loop" role in auditing AI systems.
Authors are encouraged to go beyond binary gender definitions and discuss how their work addresses the complexity of intersecting stereotypes and the diverse demographic contexts involved.
Submission Guidelines Long Papers are up to 8 pages and short Papers are up to 4 pages (excluding references and appendix). Non-Archival Submissions: Authors may opt for non-archival submission, allowing work of standard conference quality to be presented without being published in the official proceedings. Submission Link https://openreview.net/group?id=aclweb.org/AACL-IJCNLP/2026/Workshop/GeBNLP_Direct_Submission (Blind submission is required)
Important Dates Paper Submission Deadline: Wednesday, September 2, 2026 Pre-reviewed (ARR) submission deadline: Tuesday, September 29, 2026 Notification of Acceptance: Friday, October 2, 2026 Camera-Ready Version Due: Monday, October 12, 2026 Workshop Date: Monday, November 9, 2026
Paper Integrity Policy (Important)
We endorse the EMNLP 2026 Paper Integrity Policy https://2026.emnlp.org/paper-integrity-policy/. Submissions that misuse AI, include hallucinated citations, present thinly sliced contributions, or are entirely AI-generated may be desk rejected. AI-assisted writing is permitted https://www.aclweb.org/adminwiki/index.php/ACL_Policy_on_Publication_Ethics#Guidelines_for_Generative_Assistance_in_Authorship, provided authors remain responsible for the work.
Organizers Giuseppe Attanasio, Instituto de Telecomunicações, Lisbon Christine Basta, Alexandria University & HiTZ, University of the Basque Agnieszka Faleńska, University of Stuttgart Vera Neplenbroek, University of Amsterdam Debora Nozza, Bocconi University Karolina Stańczak, ETH AI Center, Zurich Marta R. Costa-jussà, FAIR, Meta Christian Hardmeier, IT university of Copenhagen
References Dai, Y., Gu, H., Wang, Y. and Wang, X., 2024, November. Mitigate extrinsic social bias in pre-trained language models via continuous prompts adjustment. In Proceedings of the 2024 conference on empirical methods in natural language processing (pp. 11068-11083).
Siddique, Z., Turner, L. and Anke, L.E., 2024, November. Who is better at math, jenny or jingzhen? uncovering stereotypes in large language models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 18601-18619).
Savoldi, B., Papi, S., Negri, M., Guerberof-Arenas, A. and Bentivogli, L., 2024, November. What the harm? quantifying the tangible impact of gender bias in machine translation with a human-centered study. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 18048-18076).