Connectionists: Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2023): Call for Papers [deadline extended]

[Benoît Frenay]

[My apologies for multiple postings]

We invite researchers working on interpretability and explainability in ML/AI, and related topics, to submit regular (14 pages, single column) or short (7 pages, single column) papers to the AIMLAI workshop that will be held at ECML/PKDD 2023. This year the workshop will feature a tutorial on Explainable GraphML.

Website: https://project.inria.fr/aimlai/
Submission link: https://easychair.org/conferences/?conf=aimlai2023
Submission deadline: June 27 June 30, 2023

The purpose of AIMLAI (Advances in Interpretable Machine Learning and Artificial Intelligence) is to encourage principled research that will lead to the advancement of explainable, transparent, ethical and fair data mining, machine learning, and artificial intelligence. AIMLAI is a workshop that seeks top-quality submissions addressing uncovered important issues related to explainable and interpretable data mining and machine learning models. Papers should present novel research results in any of the topics of interest for the workshop as well as application experiences, tools and promising preliminary ideas. AIMLAI asks for contributions from researchers, academia, and industry working on topics addressing these challenges primarily from a technical point of view, but also from a legal, ethical or sociological perspective. Besides the central topic of interpretable algorithms and explanation methods, we also welcome submissions that answer research questions like "how to measure and evaluate interpretability and explainability?" and "how to integrate humans in the machine learning pipeline for interpretability purposes?". This year's edition of AIMLAI is open to two kinds of submissions: regular papers (14 pages) and short papers (7 pages) in a single column format.

A non-exhaustive list of topics that are of interest for AIMLAI are the following:

- Interpretable ML
- Supervised and Unsupervised ML
- Explaining recommendation models
- Multimodal explanations
- Interpretable/Explainable GraphML
Transparency in AI and ML
- Ethical aspects
- Legal aspects
- Fairness issues
Methodology and formalization of interpretability
- Formal measures of interpretability
- Interpretability/complexity trade-offs
- How to evaluate interpretability
User-centric interpretability
- Explanation modules
- Interpretability and Semantics: how to add semantics to explanations?
- Human-in-the-loop to construct and/or evaluate interpretable models
- Integration of ML algorithms, infovis and man-machine interfaces

The workshop will be a full-day event that will feature a half-day tutorial on Explainable GraphML covering, among others, the topics of post-hoc explainability for graph neural networks (GNNs), knowledge graph embeddings (KGE), and explainable-by-design GraphML.

Submission Guidelines
Papers must be written in English and formatted according to the Springer LNCS (http://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines) guidelines. Regular papers must be 14 pages long maximum. Short papers are restricted to a maximum of 7 pages. In both cases the page limit excludes references, for which there is no limit. Overlength papers will be rejected without review (papers with smaller page margins and font sizes than specified in the author instructions and set in the style files will also be treated as overlength).
Authors who submit their work to AIMLAI 2023 commit themselves to present their paper at the workshop in case of acceptance. AIMLAI 2023 considers the author list submitted with the paper as final. No additions or deletions to this list may be made after paper submission, either during the review period, or in case of acceptance, at the final camera ready stage.
Condition for inclusion in the post-proceedings is that at least one of the co-authors has (in-person or virtually) presented the paper at the workshop.
All papers for AIMLAI 2023 must be submitted by using the online submission system at https://easychair.org/conferences/?conf=aimlai2023.

Program Chairs
Workshop
- Adrien Bibal, University of Colorado Anschutz Medical Campus, USA
- Tassadit Bouadi, University of Rennes/IRISA, France
- Benoît Frénay, University of Namur, Belgium
- José Oramas, University of Antwerp/imec-IDLab, Belgium

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