We seek four types of submissions:
full research papers of up to 15 pages + 3 pages of references;
short communications of up to 5 pages;
tool descriptions of up to 15 pages + 3 pages of references;
and recent previously published work (no publication, presentation only).
The tool descriptions will have to contain a link to a publicly accessible server, such as github or similar, from where the described software will be made available. The accepted papers will be published in the OASIcs series.
Submissions must be formated according to the OASIcs instructions.
Submissions will be handled by EasyChair conference management system.
https://easychair.org/conferences/?conf=ademal2023
Non-exclusive list of topics:
machine learning in general
automated deduction methods applied to machine learning
theoretical foundations of explainable AI
verifying explanations
verifying deep architectures
metrics for robustness and explanation quality
neurosymbolic reasoning
logic, calculi, and algebras in explainable AI
machine learning systems using automated deduction methods
One of the authors of each accepted paper or tool description will be expected to present it during a 30 minutes slot, out of which 25 minutes will be allocated to the presentation and 5 minutes to the subsequent discussion.