xAI 2023 : 1st World Conference on eXplainable Artificial Intelligence
Call For Papers
1st International Conference on eXplainable Artificial Intelligence (xAI 2023)
Call for papers
(26/28 July 2023, Lisbon, Portugal)
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 in the press, attracting scholars from around the world and a lay audience. An emerging field with AI is eXplainable Artificial Intelligence (xAI), devoted to the production of intelligent systems that allow humans to understand their inferences, assessments, prediction, recommendation 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 (xAI 2023) 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).
xAI 2023 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 Explainability & the semantic web 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
Methods for latent spaces interpretations 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 Explainability as prerequisite for responsible AI
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 Justification & explanations in AI systems 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 Biomedical knowledge discovery & explainability
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
Special track (workshop) proposal
Proposal submission: February 08, 2023
Notification of acceptance: February 15, 2023
Abstracts registration deadline (easy-chair): April 15, 2023
Article submission deadline (easy-chair): April 20, 2023
Notification of acceptance: May 12, 2023
Registration & camera ready: May 19, 2023
Doctoral consortium submission
Proposal registration deadline (easy-chair): April 16th 2023
Proposal submission deadline (easy-chair): April 30, 2023
Notification of acceptance: May 7, 2023
Registration: May 19, 2023
Late-breaking work & demos
Late-breaking work & demo registration (easy-chair): May 21, 2023
Late-breaking work & demo submission (easy-chair): May 28, 2023
Notification of acceptance: May 06, 2023
Panel Discussion proposals: May 21, 2023
Notification of acceptance: May 28, 2023
Registration of Panel Discussions facilitators: June 06, 2023
The World Conference on eXplainable AI 26-28 July 2023
Submitted manuscripts must be novel and not substantially duplicate existing work. Manuscripts must be written using Springer’s Lecture Notes in Computer Science (LNCS) in the format provided here. Latex and word files are admitted: however, the former is preferred (word template, latex template, latex in overleaf). All submissions and reviews will be handled electronically. The conference has a no dual submission policy, so submitted manuscripts should not be currently under review at another publication venue.
Articles must be submitted using the easy-chair platform here.
The contact author must provide the following information: paper title, all author names, affiliations, postal address, e-mail address, and at least three keywords.
The conference will not require a strict page number, as we believe authors have different writing styles and would like to produce scientific material differently. However, the following types of articles are admitted:
- full articles between 12 and 24 pages (including references)
- short articles between 6 and 12 pages (including references)
- extended abstracts between 3 and 6 pages (including references)
Full articles should report on original and substantial contributions of lasting value, and the work should concern the theory and/or practice of Explainable Artificial Intelligence (xAI). Moreover, manuscripts showcasing the innovative use of xAI methods, techniques, and approaches and exploring the benefits and challenges of applying xAI-based technology in real-life applications and contexts are welcome. Evaluations of proposed solutions and applications should be commensurate with the claims made in the article. Full articles should reflect more complex innovations or studies and have a more thorough discussion of related work. Research procedures and technical methods should be presented sufficiently to ensure scrutiny and reproducibility. We recognise that user data may be proprietary or confidential, therefore we encourage sharing (anonymized, cleaned) data sets, data collection procedures, and code. Results and findings should be communicated clearly, and implications of the contributions for xAI as a field and beyond should be explicitly discussed.
Shorter articles should generally report on advances that can be described, set into context, and evaluated concisely. These articles are not ‘work-in-progress’ reports but complete studies focused on smaller but complete research work, simple to describe. For these articles, the discussion of related work and contextualisation in the wider body of knowledge can be smaller than that of full articles.
Extended abstracts are not simply long abstracts. It should contain the definition of a problem and the presentation of a solution, comparisons to related work, and other details expected in a research manuscript but not in an abstract. An extended abstract is a research article whose ideas and significance can be understood in less than an hour. Producing an extended abstract can be more demanding than producing a full or short research article. Some things that can be omitted from an extended abstract, such as future work, details of proofs or implementation that should seem plausible to reviewers, and ramifications not relevant to the key ideas of the abstract. It should also contain enough bibliographic references to follow the main argument of the proposed research.
Anonymity for review
The submitted article (a .pdf) must be anonymous, given that the conference uses a double-blind review process. Therefore, authors must omit their names and affiliations in the submitted .pdf file and avoid obvious identifying statements. For instance, citations to the author’s prior work should be made in the third person. Failure to anonymize your submission could result in desk rejection.
Ethical & Human Subjects Considerations
The conference organisers expect authors to discuss the ethical considerations and the impact of the presented work and/or its intended application, where appropriate. Additionally, all authors must comply with ethical standards and regulatory guidelines associated with human subjects research, including using personally identifiable data and research involving human participants. Manuscripts reporting on human subjects research must include a statement identifying any regulatory review the research is subject to (and identifying the form of approval provided) or explaining the lack of required review.
Further style instructions
We ask the authors to start the reference section on a new page. Appendices count toward the page limit. Supplementary material, if any, should be linked to an external source using an anonymized URL.
All articles submitted within the deadlines and according to the guidelines will be subjected to a double-blind review. Papers that are out of scope, incomplete, or lack sufficient evidence to support the basic claims may be rejected without full review. Furthermore, reviewers will be asked to comment on whether the length is appropriate for the contribution. Each of the submitted articles will be reviewed by at least three members of the Scientific Committee.
After completion of the review process, the authors will be informed about the acceptance or rejection of the submitted work. The reviewers’ comments will be available to the authors in both cases. In case of acceptance, authors must meet the recommendations for improvement and prepare and submit the definitive version of the work up to the camera-ready paper submission deadline. In case of failure to consider the recommendations made by the reviewers, the organizing committee and the editors reserve the right not to include these works in the conference proceedings.
The article’s final version must follow the appropriate style guide and contain the authors’ data (names, institutions and emails) and the ORCID details. Submitted articles will be evaluated according to their originality, technical soundness, significance of findings, contribution to knowledge, and clarity of exposition and organisation.
Code of Ethics
Inspired by the code of ethics put forward by the Association of Computing Machinery, the programme committee, supervised by the general conference chairs and organisers, have the right to desk-reject manuscripts that perpetuate harmful stereotypes, employ unethical research practices, or uncritically present outcomes or implications that disadvantage minoritized communities. Further, reviewers of the scientific committee will be explicitly asked to consider whether the research was conducted in compliance with professional, ethical standards and applicable regulatory guidelines. Failure to do so could lead to a desk-rejection
Each accepted and presented paper will be included in the conference proceedings by Springer in Communications in Computer and Information Science. At least one author must register for the conference by the early registration deadline. The official publication date is when the publisher makes the proceedings available online. This date will be after the conference and can take a number of weeks.