This special track explores approaches to Explainable AI (XAI) based on logic and reasoning techniques. While the traditional role of logic in AI lies in knowledge representation and reasoning, it is increasingly used in other areas highly relevant to Explainable AI. This includes neuro-symbolic approaches to AI, learning from a combination of knowledge and data, explanation interfaces, and as a basis for meta-reasoning about machine learning systems. An example of meta-reasoning is explaining predictions made by classifiers. As opposed to popular heuristic-based approaches, the logic-based approach, based on prime implicants, leads to provably correct explanations and the ability to reason formally about the behaviour of a classifier, such as whether the classifier exhibits bias. Logic and reasoning can be used to study the foundations of explanations in diverse settings. This track aims to bring together researchers applying logic and reasoning within XAI. It covers various logic-based strategies for representing, validating, and implementing XAI systems, emphasising the harmonisation of logic with AI explainability.


Abduction-based explanations for machine learning models
Completeness investigation of reasons behind decisions
Logical framework for xAI in classification systems
Non-monotonic background theories for boolean classifiers explanation
Logic-based explanations for neural networks
Logic-based reasoning techniques for classifier bias
Uncertainty quantifiers for explainability
Logic and reasoning for explainable neuro-symbolic AI
Hard logical constraints for multi-label supervised neural networks
Explainability for machine reasoning techniques
Enswer set programming-based explanation approaches
XAI methods for inconsistencies handling/discovering
Explainability in theorem proving
End-to-end differentiable proving for xAI
Symbolic/sub-symbolic techniques integration for XAI
Interface development for logic-based explanations
Investigation of the impact of logic-style explanations on user decisions
Logic-style explanations under different levels of uncertainty
Logical foundations of explanations
Axiomatic foundations of explainability
Logic-based validation of heuristic for XAI
Explainable normative supervisors for reinforcement learning agents
Explanations for deontic logic

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