Semana de la IA 2026 · Taller «Crear para
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About Me
I’m Iris Dominguez-Catena, a postdoc researcher at the Public University of Navarre
(UPNA).
My academic journey at UPNA includes a Bachelor’s degree in Computer
Science (2010 to 2015), a Master’s degree in Computer Science (2018 to
2020) and a PhD (completed in 2024).
I’m currently an Assistant Proffesor at UPNA, continuing my research
on demographic bias and fairness in Artificial Intelligence.
Affiliations
Research interests
- Demographic bias
- Dataset bias
- Algorithmic fairness
- Computer vision
- Natural language processing and large language models
- Hallucations in AI
Publications
AI Fairness
Better Demographic Profiling of Datasets
through Quantification
Dominguez-Catena, I., González, P., del
Coz, J. J., Paternain, D., & Galar, M.
ECAF 2026, Ghent, Belgium
(non-archival poster) PDF
(poster)
Unmasking LAION-5B: Age, Gender, Race, and
Emotion Biases in Large-Scale Image Datasets
Dominguez-Catena, I., Galar, M., &
Paternain, D.
ICLR 2026 Workshop on Navigating and
Addressing Data Problems For Foundation Models (DATA-FM) 🔗 Link · ICLR · PDF
Leveraging Cross-Modal Information to Reduce
Gender Bias in Facial Analysis Systems
Dominguez-Catena, I., Paternain, D., Jurio,
A., & Galar, M.
IEEE Conference on Artificial
Intelligence (CAI 2026)
Facial Demography Analysis of the LAION
Dataset
Dominguez-Catena, I., Paternain, D., &
Galar, M.
EWAF2025 🔗
Link · PDF
Biased Heritage: How Datasets Shape Models in
Facial Expression Recognition
Dominguez-Catena, I., Paternain, D., Galar,
M., Defrance, M.B., Buyl, M., & De Bie, T.
IEEE Transactions on Affective
Computing. IEEE (2026) 🔗
Link · PDF
(arXiv)
DSAP: Analyzing bias through demographic
comparison of datasets
Dominguez-Catena, I., Paternain, D., &
Galar, M.
Information Fusion (Vol. 115,
p. 102760). Elsevier BV (2024) 🔗 Link · PDF (arXiv)
Less can be more: representational
vs. stereotypical gender bias in facial expression
recognition
Dominguez-Catena, I., Paternain, D., Jurio,
A., & Galar, M.
Progress in Artificial Intelligence.
Springer Science and Business Media LLC (2024) 🔗 Link · PDF
Metrics for Dataset Demographic Bias: A Case
Study on Facial Expression Recognition
Dominguez-Catena, I., Paternain, D., &
Galar, M.
IEEE Transactions on Pattern
Analysis and Machine Intelligence (2024) 🔗 Link · PDF
Gender Stereotyping Impact in Facial
Expression Recognition
Dominguez-Catena, I., Paternain, D., &
Galar, M.
Communications in Computer and
Information Science, pp. 9–22. Springer Nature Switzerland (2023)
🔗 Link · PDF (arXiv)
Assessing Demographic Bias Transfer from
Dataset to Model: A Case Study in Facial Expression
Recognition
Dominguez-Catena, I., Paternain, D., &
Galar, M.
Proceedings of the Workshop on
Artificial Intelligence Safety (AISafety 2022), IJCAI-ECAI 2022, Vienna,
Austria (2023) 🔗 Link · PDF
LLM Biases
Large Language Models Reflect the Ideology of
their Creators
Buyl, M., Rogiers, A., Noels, S., Bied, G.,
Dominguez-Catena, I., Heiter, E., Johary, I., Mara, A.-C., Romero, R.,
Lijffijt, J., & De Bie, T.
npj Artificial Intelligence (Vol. 2,
Article 7). Springer Nature (2026) 🔗 Link · PDF ·
PDF (arXiv)
OWA Operators
Learning Channel-Wise Ordered Aggregations in
Deep Neural Networks
Dominguez-Catena, I., Paternain, D., &
Galar, M.
Advances in Intelligent Systems and
Computing, pp. 1023–1030. Springer International Publishing
(2020) 🔗
Link
A Study of OWA Operators Learned in
Convolutional Neural Networks
Dominguez-Catena, I., Paternain, D., &
Galar, M.
Applied Sciences, Vol. 11, Issue 16,
p. 7195. MDPI AG (2021) 🔗 Link
Additional Feature Layers from Ordered
Aggregations for Deep Neural Networks
Dominguez-Catena, I., Paternain, D., &
Galar, M.
2020 IEEE International Conference
on Fuzzy Systems (FUZZ-IEEE). IEEE (2020) 🔗 Link
Slides