2nd World Conference on eXplainable Artificial Intelligence

Call for papers

Artificial intelligence has seen a significant shift in focus towards designing and developing intelligent systems that are interpretable and explainable. This is due to the complexity of the models, built from data, and the legal requirements imposed by various national and international parliaments. This has echoed both in the research literature and the press, attracting scholars worldwide and a lay audience. An emerging field with AI is eXplainable Artificial Intelligence (xAI), devoted to producing intelligent systems that allow humans to understand their inferences, assessments, predictions, recommendations and decisions. Initially devoted to designing post-hoc methods for explainability, eXplainable Artificial Intelligence (xAI) is rapidly expanding its boundaries to neuro-symbolic methods for producing self-interpretable models. Research has also shifted the focus on the structure of explanations and human-centred Artificial Intelligence since the ultimate users of interactive technologies are humans.

The World Conference on Explainable Artificial Intelligence is an annual event that aims to bring together researchers, academics, and professionals, promoting the sharing and discussion of knowledge, new perspectives, experiences, and innovations in the field of Explainable Artificial Intelligence (xAI). This event is multidisciplinary and interdisciplinary, bringing together academics and scholars of different disciplines, including Computer Science, Psychology, Philosophy, Law and Social Science, to mention a few, and industry practitioners interested in the practical, social and ethical aspects of the explanation of the models emerging from the discipline of Artificial intelligence (AI).

The conference organisation encourages submissions related to eXplainable AI and contributions from academia, industry, and other organizations discussing open challenges or novel research approaches related to the explainability and interpretability of AI systems. Topics include, and are not limited to:

Technical methods for XAI
Action Influence Graphs
Agent-based explainable systems
Ante-hoc approaches for interpretability
Argumentative-based approaches for xAI
Argumentation theory for xAI
Attention mechanisms for xAI
Automata for explaining RNN models
Auto-encoders & latent spaces explainability
Bayesian modelling for interpretability
Black-boxes vs white-boxes
Case-based explanations for AI systems
Causal inference & explanations
Constraints-based explanations
Decomposition of NNET-models for XAI
Deep learning & XAI methods
Defeasible reasoning for explainability
Evaluation approaches for XAI-based systems
Explainable methods for edge computing
Expert systems for explainability
Sample-centric and dataset-centric explanations
Explainability of signal processing methods
Finite state machines for explainability
Fuzzy systems & logic for explainability
Graph neural networks for explainability
Hybrid & transparent black box modelling
Interpreting & explaining CNN Networks
Interpretable representational learning
Explainability & the Semantic Web
Model-specific vs model-agnostic methods
Neuro-symbolic reasoning for XAI
Natural language processing for explanations
Ontologies & taxonomies for supporting XAI
Pruning methods with XAI
Post-hoc methods for explainability
Reinforcement learning for enhancing XAI
Reasoning under uncertainty for explanations
Rule-based XAI systems Robotics & explainability
Sample-centric & Dataset-centric explanations
Self-explainable methods for XAI
Sentence embeddings to xAI semantic features
Transparent & explainable learning methods
User interfaces for explainability
Visual methods for representational learning
XAI Benchmarking
XAI methods for neuroimaging & neural signals
XAI & reservoir computing

Ethical Considerations for XAI
Accountability & responsibility in XAI
Addressing user-centric requirements for XAI
Trade-off model accuracy & interpretability
Explainable Bias & fairness of XAI systems
Explainability for discovering, improving, controlling & justifying
Moral Principles & dilemma for XAI
Explainability & data fusion
Explainability/responsibility in policy guidelines
Explainability pitfalls & dark patterns in XAI
Historical foundations of XAI
Moral principles & dilemma for XAI
Multimodal XAI approaches
Philosophical consideration of synthetic explanations
Prevention/detection of deceptive AI explanations
Social implications of synthetic explanations
Theoretical foundations of XAI
Trust & explainable AI The logic of scientific explanation for/in AI
Expected epistemic & moral goods for XAI
XAI for fairness checking
XAI for time series-based approaches

Psychological Notions & concepts for XAI
Algorithmic transparency & actionability
Cognitive approaches for explanations
Cognitive relief in explanations
Contrastive nature of explanations
Comprehensibility vs interpretability
Counterfactual explanations
Designing new explanation styles
Explanations for correctability Faithfulness & intelligibility of explanations
Interpretability vs traceability
explanations Interestingness & informativeness
Irrelevance of probabilities to explanations
Iterative dialogue explanations Local vs. global interpretability & explainability Local vs global interpretability & explainability
Methods for assessing explanations quality
Non-technical explanations in AI systems
Notions and metrics of/for explainability
Persuasiveness & robustness of explanations
Psychometrics of human explanations
Qualitative approaches for explainability
Questionnaires & surveys for explainability
Scrutability & diagnosis of XAI methods Soundness & stability of XAI methods

Social examinations of XAI
Adaptive explainable systems
Backwards & forward-looking responsibility forms to XAI
Data provenance & explainability
Explainability for reputation
Epistemic and non-epistemic values for XAI
Human-centric explainable AI
Person-specific XAI systems
Presentation & personalization of AI explanations for target groups
Social nature of explanations

Legal & administrative considerations of/for XAI
Black-box model auditing & explanation
Explainability in regulatory compliance
Human rights for explanations in AI systems
Policy-based systems of explanations
The potential harm of explainability in AI
Trustworthiness of XAI for clinicians/patients
XAI methods for model governance
XAI in policy development
XAI for situational awareness/compliance behavior

Safety & security approaches for XAI
Adversarial attacks explanations
Explanations for risk assessment
Explainability of federated learning
Explainable IoT malware detection
Privacy & agency of explanations
XAI for Privacy-Preserving Systems
XAI techniques of stealing attack & defence
XAI for human-AI cooperation
XAI & models output confidence estimation

Applications of XAI-based systems
Application of XAI in cognitive computing
Dialogue systems for enhancing explainability
Explainable methods for medical diagnosis
Business & Marketing
XAI systems for healthcare
Explainable methods for HCI
Explainability in decision-support systems
Explainable recommender systems
Explainable methods for finance & automatic trading systems
Explainability in agricultural AI-based methods
Explainability in transportation systems
Explainability for unmanned aerial vehicles
Explainability in brain-computer interfaces
Interactive applications for XAI
Manufacturing chains & application of XAI
Models of explanations in criminology, cybersecurity & defence
XAI approaches in Industry 4.0
XAI systems for health-care
XAI technologies for autonomous driving
XAI methods for bioinformatics
XAI methods for linguistics/machine translation
XAI methods for neuroscience
XAI models & applications for IoT
XAI methods for XAI for terrestrial, atmospheric, & ocean remote sensing
XAI in sustainable finance & climate finance
XAI in bio-signals analysis