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Workshop
Workshop on Semantic Knowledge-based Explainability of Artificial Intelligence - SKEAI 2025

4 - 6 April, 2025 - Porto, Portugal

In conjunction with the 27th International Conference on Enterprise Information Systems - ICEIS 2025


CHAIR

Adeel Ahmad
Universite du Littoral Cote d'Opale
France
 
Brief Bio
Adeel Ahmad is a Research Scientist at the Laboratoire d’Informatique Signal et Image de la Côte d’Opale, France. He holds a PhD in Computer Science from the Université du Littoral Côte d’Opale, Calais, and has served as a research engineer and associate professor in computer science since 2011. His research focuses on enhancing the explainability of artificial intelligence (AI) systems. He has published work related to the Fourth Industrial Revolution (Industry 4.0), particularly in automating the analysis of change impact propagation for the qualitative evaluation and improvement of process models. His research interests include rule-based business intelligence and advancing explainability in machine learning.

SCOPE

The workshop aims to bring together researchers, practitioners, and domain experts to exchange knowledge, address challenges, and outline future directions for developing explainable, interpretable, and transparent AI systems. It focuses on advancing Explainable Artificial Intelligence (XAI) by incorporating knowledge and semantics as core components. Contributions will address “demystifying the black-box” nature of AI and tailoring explanations to diverse user expertise levels, supporting equitable and fair decision-making for long-term sustainability. The workshop seeks to overcome the challenges of embedding semantic abstractions into intelligent information systems. These efforts are intended to foster trust, improve debugging, and encourage the adoption of AI in critical domains such as healthcare, finance, policy-making, and education.

TOPICS OF INTEREST

Topics of interest include, but are not limited to:

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

IMPORTANT DATES

Paper Submission: January 30, 2025
Authors Notification: February 13, 2025
Camera Ready and Registration: February 21, 2025

WORKSHOP PROGRAM COMMITTEE

Nadia Abchiche-Mimouni, UNS & Polytech Nice Sophia, France
M. K. Abdi, Laboratoire RIIR, Université Oran1, Algeria
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
Nicolas Labroche, LIFAT, Université de Tours, France
Arnaud Lewandowski, Université Lille Nord de France, France
Yazan Mualla, CIAD, Univ. Bourgogne Franche-Comté, UTBM, France

(list not yet complete)

PAPER SUBMISSION

Prospective authors are invited to submit papers in any of the topics listed above.
Instructions for preparing the manuscript (in Word and Latex formats) are available at: Paper Templates
Please also check the Guidelines.
Papers must be submitted electronically via the web-based submission system using the appropriated button on this page.

PUBLICATIONS

After thorough reviewing by the workshop program committee, all accepted papers will be published in a special section of the conference proceedings book - under an ISBN reference and on digital support.
All papers presented at the conference venue will be available at the SCITEPRESS Digital Library (http://www.scitepress.org/DigitalLibrary/).
SCITEPRESS is a member of CrossRef (http://www.crossref.org/) and every paper is given a DOI (Digital Object Identifier).

SECRETARIAT CONTACTS

ICEIS Workshops - SKEAI 2025
e-mail: iceis.secretariat@insticc.org
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