EXPLAINS 2024: International Conference on Explainable AI for Neural and Symbolic Methods

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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</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/1">Dou Sun</a> in 2024-04-06</div> </div> </div>  <div class="portlet" id="yw2"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-th"></i> Related Conferences</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw3" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> 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EXPLAINS 2024: International Conference on Explainable AI for Neural and Symbolic Methods