About me
Dominik Kowald is professor for AI-based information retrieval in digital humanities
at University of Graz (starting in October 2025) as well as
research area head of the FAIR AI group
at Know Center Research GmbH, one of Europe's leading research centers for trustworthy AI.
He also holds a venia docendi in applied computer science at the Institute of Human Centred Computing
of Graz University of Technology.
He has finished his PhD (with distinction) in October 2017 on psychology-informed recommender systems
based on the cognitive architecture ACT-R.
Additionally, in June 2024, he has finished his habilitation (post-doctoral thesis) on the topic of
transparency, privacy, and fairness aspects of recommender systems.
He is key researcher in the Interfaces of Agent-Centric AI FFG COMET module,
and in several other applied research projects (see full CV for details).
His research interests are in the fields of trustworthy and reproducible AI, recommender systems, information retrieval, fairness and biases in AI, as well as digital humanities,
in which he has published more than 100 papers in top-tier journals and conferences.
His research on fairness in AI and bias in recommender systems was also awarded with the TU Graz Mind-the-gap gender and diversity award in 2022,
and was presented in several news outlets, e.g., by APA science.
Full CV: ( .pdf)
Open theses: ( link)
Selected publications
- Semmelrock, H., Ross-Hellauer, T., Kopeinik, S., Theiler, D., Haberl, A., Thalmann, S., & Kowald, D. (2025). Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers. AI Magazine, 46(2). ( .pdf)
- Burke, R., Adomavicius, G., Bogers, T., Di Noia, T., Kowald, D., Neidhardt, J., Özgöbek, Ö., Pera, S., Tintarev, N., & Ziegler, J. (2025). De-centering the (Traditional) User: Multistakeholder Evaluation of Recommender Systems. International Journal on Human Computer Studies. ( .pdf)
- Scher, S., Kopeinik, S., Truegler, A., & Kowald, D. (2023). Modelling the Long-Term Fairness Dynamics of Data-Driven Targeted Help on Job Seekers. Nature Scientific Reports. ( .pdf)
- Muellner, P., Lex, E., Schedl, M., & Kowald, D. (2023). ReuseKNN: Neighborhood Reuse for Differentially-Private KNN-Based Recommendations. ACM Transaction on Intelligent Systems and Technology. ( .pdf)
- Kowald, D., Muellner, P., Zangerle, E., Bauer, C., Schedl, M. & Lex, E. (2021). Support the Underground: Characteristics of Beyond-Mainstream Music Listeners. EPJ Data Science. ( .pdf) ( blog)
- Kowald, D., Schedl, M., & Lex, E. (2020). The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study. In Proceedings of the 42nd European Conference on Information Retrieval (ECIR'2020). Springer. ( .pdf)
- Kowald, D., Pujari, S., & Lex, E. (2017). Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach. In Proceedings of the 26th International World Wide Web Conference (WWW'2017). ACM. ( .pdf)
- Kowald, D., Seitlinger, P., Trattner, C., & Ley, T. (2014). Long Time no See: The Probability of Reusing Tags as a Function of Frequency and Recency. In Proceedings of the companion publication of the 23rd international conference on World wide web companion (WWW'2014), pp. 463-468. International World Wide Web Conferences Steering Committee. ( .pdf)