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).
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