SCOPE

In the future people will collaborate more and more with machines to solve complex problems using AI techniques. Such a collaboration requires adequate communication, trust, clarity and understanding.
eXplainalbe AI (XAI) aims at addressing such challenges by combining the best of symbolic AI and Machine Learning including neural models, evolutionary computing and fuzzy systems. Such topic has been studied for years by all different communities of AI, with different definitions, evaluation metrics, motivations and results. In addition to technology, this involves social and legal issues as well as a wide range of real-world applications and domains. Both interpretability by design methods and post-hoc methods for explaining complex models have been proposed and investigated. Research has also redirected its emphasis on the structure of explanations and human-centered Artificial Intelligence, recognizing that the ultimate users of interactive technologies are humans.

This conference aims at attracting different research perspectives to promote debate. It intends to be a major multidisciplinary and interdisciplinary forum, bringing together academics and scholars of different disciplines, interested in the study, analysis, design, modelling and implementation of interpretable and explainable AI systems. Contributions are welcome both in addressing theoretical issues and in a broad range of application fields.

CONFERENCE AREAS

Each of these topic areas is expanded below but the sub-topics list is not exhaustive. Papers may address one or more of the listed sub-topics, although authors should not feel limited by them. Unlisted but related sub-topics are also acceptable, provided they fit in one of the following main topic areas:

1. TECHNOLOGY
2. SOCIAL AND LEGAL ISSUES
3. APPLICATIONS

AREA 1: TECHNOLOGY

    Generative AI vs Interpretability
    Explainable Generative AI
    XAI using Machine Learning
    Deep Learning and XAI
    Fuzzy Systems and Logic for XAI
    Knowledge Graphs in XAI
    Explainable Graph Neural Networks
    Explainable Neuro-Symbolic Reasoning
    Evaluation of Explainability
    Argumentative-Based Approaches for XAI
    Bayesian Modelling for Interpretability
    Explainable Edge Computing
    Human-Computer Interfaces Supporting XAI
    Natural Language Processing and XAI
    XAI for the Semantic Web
    Ontologies Supporting XAI
    Metrics for Explanations
    XAI Benchmarking
    Evolutionary XAI Approaches
    XAI for Evolutionary Computing
    Post-Hoc Methods for Explainability
    Model-Specific vs Model-Agnostic Methods for Explainability

AREA 2: SOCIAL AND LEGAL ISSUES

    Ethical Concerns of XAI
    Accountability and Responsibility
    Explainable Bias and Fairness of XAI Systems
    Model Accuracy and Interpretability
    Explainability Pitfalls and Problems in XAI
    Prevention/Detection of Deceptive AI Explanations
    Social Implications of Synthetic Explanations
    Trust Management and Reputation
    Regulatory Compliance
    Adversarial Attacks Explanations

AREA 3: APPLICATIONS

    Healthcare and Biomedical Sciences
    Human/AI Cooperation
    Decision-Support Systems
    Recommender Systems
    Computer Vision Applications
    Robotics and Control Systems
    Explaining Object and Obstacle Detection
    Explainable Methods for Finance
    Explaining Project Risk
    Explainability in Transportation Systems
    Supply Chains and Industry 4.0
    Internet of Things
    Security and Privacy
    Privacy-Preserving Systems