AoIR Pre-Conference Workshop
Women* in AI Research: A Critical Data Sprint
AI has become both an object of inquiry and a site of concern within the humanities and social sciences. Under the umbrella of Critical AI Studies (Raley & Rhee, 2023), scholarship has shown how biases, for instance embedded in training data, can lead to discriminatory outcomes (Dastin, 2018; Otokiti, 2025). At the same time, research on academic knowledge production documents persistent gendered inequalities: progress toward closing the publishing gender gap has slowed across fields (Jemielniak, 2024), women’s work is cited less frequently even when publication rates are higher (Jansen et al., 2025), and female scholars are less often represented as expert voices in the public domain (Siegumfeldt, 2026). Whether and how these patterns structure emerging AI research in the humanities and social sciences remains an open empirical question.
This workshop responds to these layered inequities by turning critical inquiry inward. Rather than focusing solely on how AI reproduces bias, it examines how AI research itself is structured by gendered patterns of (mis)representation and (mis)recognition.
Objectives: The workshop pursues three interrelated objectives.
To raise awareness of gendered inequalities in AI research across academic citation, research funding, and public discourse.
To introduce participants to exploratory data sprints as a collaborative method for critically examining power asymmetries and bias in knowledge production, recognition, and dissemination.
To collaboratively generate preliminary empirical mappings of gender (mis)representation and (mis)recognition in AI research, producing public-facing documentation that can inform further interventions.
Format and Operation: This half-day (three-hour) workshop combines conceptual framing with collaborative work centered around an exploratory data sprint, inspired by methods developed by Omena et al. (2022). Participants will work in groups on one of three empirical foci:
Authorship and Citation: Examining gendered authorship patterns and citation practices in AI-related humanities and social science scholarship
Research Funding: Exploring gender differences in the allocation of funding for AI research across major funding bodies
Public Recognition: Mapping the representation of female experts in public discourse on AI
Workshop facilitators will provide pre-prepared datasets, introduce relevant analytical tools and tools for visualization, and guide participants through the data sprint process. No advanced technical skills are required.
The workshop concludes with a collective synthesis and discussion (approx. 30 minutes), reflecting on empirical findings, methodological challenges, and avenues for follow-up work.
Participants: The workshop is aimed at:
Scholars across various career stages in the humanities and social sciences
Researchers whose work focuses on AI, digital technologies, critical data studies, sciences and technology studies
Participants with an interest in gender analysis, feminist methods, and collaborative or computational research approaches
To ensure meaningful collaboration, participation will be capped at 24 participants, who will be divided into three working groups during the data sprint.
Facilitators are Nadja Schaetz (University of Hamburg) and Vasilisa Kuznetsova (University of Bremen)
Outcomes and Follow-Up: The workshop will produce exploratory empirical mappings and visual documentation of gender inequities in AI research practices. These outputs will inform a paper addressing identified inequities and a subsequent wikithon aimed at actively reconfiguring gender imbalances in public knowledge infrastructures. Participation in any follow-up publications or activities will be optional and based on transparent contribution criteria.
You can indicate your interest in participating in the workshop via AoIR’s registration system. If you’re already registered for the conference, please follow this link: https://www.members.aoir.org/Sys/Poll/75267
References
Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters.https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G
Omena, J. J.; Cano-Orón, L.; Gobbo, B. & Flores, A. M. (2022). What are data sprints for?. Dígitos. Revista de Comunicación Digital, 8: 9-30. DOI: 10.7203/drdcd.v1i8.253
Otokiti, A. U., Shih, H.-J., & Williams, K. S. (2025). Gender and racial bias unveiled: Clinical artificial intelligence (AI) and machine learning (ML) algorithms are fanning the flames of inequity. Oxford Open Digital Health, 3, oqaf027. https://doi.org/10.1093/oodh/oqaf027
Raley, R., & Rhee, J. (2023). Critical AI: A Field in Formation. American Literature, 95(2), 185–204. https://doi.org/10.1215/00029831-10575021
Siegumfeldt, P. (2026). Se listen: Her er Danmarks 50 mest citerede forskere i 2025. DM. Retrieved 4 February 2026, from https://dm.dk/forskerlisten2025