View source for XAI-2024

{{Event
|Acronym=xAI-2024
|Title=The 2nd World Conference on eXplainable Artificial Intelligence
|Ordinal=2
|Type=Conference
|Field=Artificial Intelligence
|Start date=2024/07/17
|End date=2024/07/19
|Submission deadline=2024/03/05
|Homepage=https://xaiworldconference.com/2024/
|City=Valletta
|Country=Malta
}}
'''2nd World Conference on eXplainable Artificial Intelligence

https://xaiworldconference.com/2024/call-for-papers/

'''Valletta, Malta, 17-19 July, 2024''''''

'''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 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 behaviour

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