AREA 1: FOUNDATIONAL METHODS FOR EXPLAINABILITY

• Model-agnostic and Model-specific Approaches to Explanation
• Use of Explainable and/or Interpretable Surrogate Models for Black-box AI Systems
• Techniques for Improving Transparency in Deep Neural Networks

AREA 2: KNOWLEDGE-DRIVEN EXPLAINABILITY

• Integration of Knowledge Graphs and Ontologies for Semantic Explanations
• Tools and Frameworks for Explicit Representation of Knowledge in AI Systems
• Semantic-level Abstractions for User-aligned Explanations

AREA 3: USER-CENTRIC, HUMAN-AI INTERACTION, AND DOMAIN-SPECIFIC EXPLAINABILITY

• Designing User-aligned Explanations for Diverse Expertise Levels
• AI Explainability for Critical Applications Such as Diagnostic Systems (E.G., Healthcare)
• AI Explainability for Critical Applications Such as Autonomous Systems (e.g., Decision Transparency)
• AI Explainability for Critical Applications Such as Social Systems (e.g., Policy-making, Ecological Transition, or Funds Allocation)
• AI Explainability for Critical Applications Such as Tailored Explanations for Non-technical Users, Such as Doctors, Engineers, and Policy-makers

AREA 4: CHALLENGES IN BLACK-BOX AI

• Addressing the Inherent Opacity of Deep Learning and Other Black-box Models
• Trade-offs Between Accuracy and Explainability in AI Systems
• Explainability Challenges in Large-scale, Real-world AI Deployments

AREA 5: HISTORICAL AND MODERN APPROACHES

• Lessons from Traditional Knowledge-based Systems on Reasoning Explanations
• Advances in Description Logic, Ontologies, and Their Role in Explainability
• Bridging Classical and Modern AI Paradigms for Comprehensive Explanations

AREA 6: EMERGING TECHNIQUES AND INNOVATIONS

• Explainability in Federated and Distributed AI Systems
• Methods to Enhance Transparency in Multi-modal AI Systems
• Explainability in Generative AI Models (e.g., LLMs and Diffusion Models)

AREA 7: EXPLAINABILITY FRAMEWORKS AND STANDARDS

• Guidelines and Best Practices for Developing Explainable AI
• Standardization of Explainability Metrics and Benchmarks
• Explainability in the Context of Ethics, Governance and Policy-making

AREA 8: FUTURE DIRECTIONS IN EXPLAINABILITY RESEARCH

• Novel Paradigms for Embedding Knowledge into Explainable AI
• Explainability in Hybrid AI Systems Combining Symbolic and Sub-symbolic Methods
• The Role of Explainability in Advancing Human-AI Collaboration
• Cross-disciplinary Approaches Combining AI with Cognitive Science and XAI-assisted Decision Making

Nadia Abchiche-Mimouni, UNS & Polytech Nice Sophia, France
Julien Aligon, IRIT, Universite Toulouse Capitole, France
Henri Basson, LISIC, Université du Littoral Côte d'Opale, France
Mourad Bouneffa, LISIC, ULCO, France
Grégory Bourguin, Université du Littoral Côte d'Opale, France
Alexandre Chanson, LIFAT, Université de Tours, France
Moncef Garouani, IRIT, Université Toulouse Capitole, France
Hanaa Hachimi, National School of Applied Sciences, Ibn Tofail University Campus, Morocco
Mohamed Hamlich, ISSIEE, ENSAM, University of Hassan II, Morocco
Abdi Mustapha Kamel, Exact and Applied Sciences Faculty, Computer Science Department, Universite Oran1, Algeria
Nicolas Labroche, LIFAT, Université de Tours, France
Arnaud Lewandowski, Université Lille Nord de France, France
Yazan Mualla, CIAD, Univ. Bourgogne Franche-Comté, UTBM, France
Andy E. Williams, Nobeah Foundation, Kenya

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