Are you interested in contributing research for underserved communities? Don't miss the EEAMO Conference 2026, the 6th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization.
This event will showcase work across the research-to-practice pipeline, aiming to ensure that algorithmic systems serve a broadly beneficial role in society by advancing equity and expanding access to opportunities for underserved communities.
relAI research will be featured at the International Conference on Learning Representations (ICLR), which will take place this year at the Riocentro Convention and Event Center in Rio de Janeiro, Brazil, from April 23rd to 27th, 2026. ICLR is one of the leading conferences with significant impact and reputation in machine learning and artificial intelligence research.
relAI Publications at ICLR
Seventeen publications from relAI will be presented at the conference, fifteen of them in the main track.
We are happy to announce that Barbara Plankhas joined relAI as a Fellow!
Barbara is Full Professor for AI and Computational Linguistics at LMU Munich, where she holds the Chair in AI & Computational Linguistics and co-directs the Center for Information and Language Processing (CIS). She also serves as Head of the Munich AI & NLP lab (MaiNLP) and visiting Professorship at the IT University of Copenhagen
Her research on robustness, domain shift, and human label variation aligns well with relAI’s Algorithmic Decision Making research area. This work explores how AI systems learn and make decisions in the face of uncertainty and disagreement. Additionally, her emphasis on interpretability, reasoning, and trustworthy evaluation provides essential foundations for developing reliable, fair, and transparent algorithmic decision systems.
At relAI, Barbara will contribute by participating in seminars, workshops, and panels, as well as offering career advice.
A warm welcome! 🤝
/ mc
🎉 relAI is proud to announce that the International Association for Dental, Oral, and Craniofacial Research (IADR) has named relAI Fellow Falk Schwendicke as the recipient of the 2026 IADR Distinguished Scientist William H. Bowen Research in Dental Caries Award 🦷 .
IADR is a nonprofit organization dedicated to advancing dental, oral, and craniofacial research for global health and well-being. This esteemed IADR award recognizes exceptional and innovative contributions to our understanding of caries etiology and the prevention of dental caries. It is one of the 17 IADR Distinguished Scientist Awards and is considered one of the highest honors bestowed by the organization.
Falk Schwendicke’s Research Achievements
His early work focused on minimally invasive and evidence-based caries management, particularly regarding selective carious tissue removal and its economic evaluation. This research has laid the groundwork for contemporary treatment guidelines. His recent studies have increasingly emphasized the integration of emerging technologies to overcome challenges in caries detection and management. Notably, he has been a pioneer in employing advanced artificial intelligence (AI) applications for radiographic analysis, diagnostic support, and predictive modeling.
A significant achievement in his career was leading a randomized controlled trial that evaluated AI-assisted caries detection. This study set new standards for clinical research in the field and informed subsequent cost-effectiveness analyses. Schwendicke also participates in numerous editorial and review roles and has presented at the IADR General Session and various scientific meetings. He has authored over 500 peer-reviewed publications and 30 book chapters and is ranked among the top 1% of most-cited dental researchers worldwide, according to the Stanford global ranking.
In recent years, he has made significant contributions to AI-based hematological cytology. His focus on the interpretability of models trained on patient data to make predictions in a biomedical context 🩺 closely aligns with relAI's central themes of safety and responsibility. His innovative multiple instance learning models facilitate the investigation of relevant cells for disease prediction, while sparse autoencoders help correlate image features with diagnostic concepts. Additionally, his work on linking images and language enables direct comparisons between understandable human terms and cellular patterns within gigabyte-sized digital scans. At relAI, he will support students through lectures, mentoring, and participation in events.
/ mc
🎉 Congratulations!
relAI is thrilled to announce that Frauke Kreuter, relAI Fellow and member of the relAI Steering Committee, has been elected a Fellow of the American Association for the Advancement of Science (AAAS). AAAS is the world's largest general scientific society and publisher of the journal Science. Founded in 1848, this non-profit international organization promotes scientific freedom, responsibility, education, and collaboration to improve humanity, serving over 120,000 members.
Being elected as a Fellow is a prestigious honor that recognizes individuals whose contributions to advancing science and its applications in service to society have distinguished them among their peers and colleagues.
📖 The article presents the first exploratory case study evaluating the quality of simplified radiology reports generated by the large language model (LLM) ChatGPT. Radiologists rated the reports as generally high quality but also identified errors that could lead to harmful patient interpretations. The findings highlight both the potential and the limitations of early large language models in clinical communication: while simplified reports can enhance accessibility, medical expert supervision and domain-specific adaptation are vital to ensure patient safety.
💡 The study, first published as a preprint in December 2022, was among the earliest scientific assessments of ChatGPT's ability to simplify radiology reports for patients. Since then, a rapidly growing body of research has explored the role of large language models in medical text simplification.
His lab focuses on the fundamental question of how to develop a scalable approach to building intelligent humanoid robots while also providing formal safety guarantees for reliable deployment in our daily lives. This research direction aligns with relAI's goal of creating safe and secure AI made in Germany. Moreover, his work on ethics in robotics 🤖 complements relAI's mission by emphasizing the importance of ethical considerations in the development of reliable AI.
As a fellow, he will contribute to the relAI curriculum by delivering lectures to students and helping them gain practical experience through internships.
A warm welcome! 🤝
/ mc
🙌 We warmly welcome Valentin Hofmann, an incoming tenure-track assistant professor at LMU Munich in Information and Language Processing using AI methods.
Valentin Hofmann's research lies at the intersection of AI, natural language processing, and computational social science. A primary focus of his work is to enhance the robustness, safety, and fairness of large language models, particularly regarding social biases and their implications for reliable AI.
His studies on large language models are relevant to the relAI Research Area of 🤖 Robotics and Interactive Systems, as these models increasingly serve as essential components of interactive, human-facing AI systems, such as conversational assistants, where reliability is crucial. Furthermore, his research directly aligns with the relAI Central Themes of Safety and Responsibility by investigating and mitigating social biases and their potential risks in deployed AI language technologies. As a fellow, he will contribute to relAI through teaching, mentoring, and community activities.
/ mc
👏 Congratulations to relAI PhD Student Jan Simson and relAI Fellow Prof. Christoph Kern!
The German Society for Online Research (DGOF) annually recognizes outstanding scientific contributions to the advancement of the methods of online research through the DGOF Best Paper Award.
Their award-winning paper emphasizes the importance of transparency and accessibility in key decisions throughout the machine learning pipeline for the general public. It introduces a participatory approach to help navigate the multiverse of design choices, advocating for the democratization of essential decisions rather than simply focusing on optimization.
📖 To the article:
Simson, J., et al. (2025). Preventing Harmful Data Practices by using Participatory Input to Navigate the Machine Learning Multiverse. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 806, 1-30. https://doi.org/10.1145/3706598.3713482
