Date : 2026-03-24
Lieu : Tampere, Finland
Dear Colleagues,
We are pleased to announce the Call for Papers for XAI4Science2026 workshop, to be held on 24th March 2026 at Tampere (Finland) in conjunction with EDBT.
We invite scholars, researchers, and practitioners to share innovative work, present new findings, and engage in meaningful discussions on emerging developments in the field of Explainable Machine Learning (XAI)
Research topics include, but are not limited to:
-Generative AI methods for automatically propose new hypothesis compatible with the available scientific data and domain knowledge
-Explanation methods for validating or contrasting scientific hypotheses by uncovering cause-effect relationships
-Interpretable AI methods to discover spatial and temporal dynamics in complex systems
-Formal verification to bridge the gap between data-driven decisions and domain-specific constraints
-Multimodal Explanations using graphical (visual), symbolic (equations), and sentential (verbal) interfaces
-Quantitative evaluation of explanations utility in scientific domains
-Exploratory processes of explanations involving complex interactions between human, technical, and organizational factors
We are welcoming submissions of short papers (4 pages limit) and regular papers (6 pages limit), including all figures and tables. Unlimited pages are allowed for references and appendices in the same PDF as the main paper.
Submission Deadline : 05/01/2026
Notification : 02/02/2026
Camera-ready: 17/02/2026
Please find all the details of the workshop below:
International Workshop on Explainable Data Science and Machine Learning for the Sciences (XAI4Science)
24 March 2026, Tampere, Finland In conjunction with EDBT/ICDT 2026
https://www.etis-lab.fr/XAI4Science2026
Over the last couple of decades, the increasing availability of advanced computational resources and big scientific data boosted data-driven methods in scientific discovery and innovation. From neuroscience and astrophysics, to medicine and pharmaceutics, chemistry and material sciences up to weather and climate sciences, scientists currently process large volumes of experimental data and employ data science and machine learning techniques to validate and generate scientific hypotheses. Unfortunately, existing AI systems used to engineer and analyse data are mainly opaque, i.e., it is difficult to understand why they return a specific output or what they could return if input data were slightly different. They typically made automated decisions by fixating on a particular hypothesis under investigation without providing evidence for or against it. Recent advances in explainable artificial intelligence (XAI) aim to bridge the gap between a human cognitive decision-making process and AI systems. However, XAI methods mainly focus on understanding AI model behavior rather than how to exploit it for discovering new human knowledge. Their impact in complex problem solving is currently limited by the lack of completeness, robustness and universality across AI models, data modalities and scientific pipelines. The XAI4Science workshop aims to bring together researchers, practitioners, and domain experts working at the intersection of data science, machine learning and scientific disciplines for discussing advances in XAI methods that can effectively and efficiently support scientific discovery. The workshop will include a wide range of explanation techniques (i) for analysing diverse data modalities (e.g., from image, to time series and graphs) (ii) using several AI models of increasing generality (e.g., trained from scratch, pre-trained or foundation models) (iii) via complex laboratory pipelines with scientists in the loop.
Organizers:
Vassilis Christophides (ETIS, CNRS, ENSEA), Jin-Song Dong (National University of Singapore), Nicolas Labroche (Univ. of Tours), Evaggelia Pitoura (Univ. of Ioannina, Archimedes Research Unit of Athena RC), Céline Robardet (INSA Lyon, LIRIS), Yongfeng Zhang (Rutgers University)
PC:
Julien Aligon (Université Toulouse Capitole, IRIT Lab, SIG Team)
Alexandre Chanson (Université de Tours, LIFAT Lab)
Emmanuel Doumard (Université de Tours, LIFAT Lab)
Moncef Garouani (Université Toulouse Capitole, IRIT Lab, SIG Team)
Leilani Gilpin (University of California Santa Cruz, AIEA Lab)
Riccardo Guidotti (University of Pisa, KDD Lab)
Matthijs van Leeuwen (Leiden University, LIACS Lab)
Michele Linardi (CY Cergy Paris Université / ENSEA, ETIS Lab)
Marie-Jeanne Lesot (Sorbonne Université / LIP6)
Patrick Marcel (Université d’Orléans, LIFO Lab)
Christophe Marsala (Sorbonne Université / LIP6)
Guillaume Renton (ENSEA, ETIS Lab)
Konstantinos Stefanidis (Tampere University, Data Science Subunit)
Simone Stumpf (University of Glasgow, School of Computing Science)
Juntao Tan (Rutgers University, Computer Science Department)
Aikaterini Tzompanaki (CY Cergy Paris Université / ENSEA, ETIS Lab)
Eirini Ntoutsi (Bundeswehr University Munich, AIML)
Best Regards,
Katerina Tzompanaki (on behalf of the organizers).
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