AIMLAI@ECML/PKDD 2025 - Call for Papers
Benoît Frenay
We invite researchers working on interpretability and explainability in machine learning and artificial intelligence, as well as related topics, to submit regular papers (14 pages, single column) or short papers (7 pages, single column) to the AIMLAI Workshop, to be held in conjunction with ECML/PKDD 2025.
Website: https://project.inria.fr/aimlai/
Submission link: https://cmt3.research.microsoft.com/ECMLPKDDWorkshopTrack2025/Submission/Index
Submission deadline: June 14, 2025
Workshop: September 15, 2025
AIMLAI (Advances in Interpretable Machine Learning and Artificial Intelligence) aims to foster principled research and discussion on building explainable machine learning and AI systems . We invite contributions from researchers in academia and industry that approach the challenges of achieving explainability and interpretability in AI systems from technical, as well as legal, ethical, or sociological perspectives.
Submissions may include:
• Novel research results
• Application experiences
• Tools and systems
• Preliminary ideas with promising potential
Topics of Interest (non-exhaustive)
Interpretable Machine Learning
• Interpretable-by-design models
• Explainable recommendation systems
• Multimodal explanations
• Explainability for large language models (LLMs)
• LLMs as tools for explainability
• Mechanistic interpretability
Transparency, Ethics, Fairness and Privacy
• Ethical implications of AI/ML
• Legal frameworks and compliance
• Fairness and bias mitigation
• Interplay of explainability and privacy
Methodology & Evaluation
• Formal measures of interpretability and explainability
• Trade-offs between interpretability and model complexity
• Evaluation frameworks
• User-centered interpretability
Explanation Modules & Human Integration
• Semantics in explanations
• Human-in-the-loop systems
• Combining ML, information visualization, and human-computer interaction
We look forward to your contributions and to engaging discussions at AIMLAI 2025!
Tassadit Bouadi (University of Rennes/IRISA, France)
Benoît Frénay (University of Namur, Belgium)
Luis Galárraga (Inria/IRISA, France)
Megha Khosla (TU Delft)
José Oramas (University of Antwerp/imec-IDLab, Belgium)
Benoît Frénay
Professor
Faculty of Computer Science
Human-Center Machine Learning (HuMaLearn)
UNamur / NaDI / PReCISE
https://directory.unamur.be/staff/bfrenay
https://humalearn.info.unamur.be
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