xAI 2023

 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

Important dates

*All dates are Anywhere on Heart time (AoE)

Article (main track & special tracks)
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
Publication (Springer CCIS series)	August 2023
*full, short and extended abstract articles

Doctoral consortium (DC) proposal
DC Proposal registration deadline (easy-chair):	April 16th 2023
DC Proposal submission deadline (easy-chair):	April 30, 2023
Notification of acceptance:	May 7, 2023
Registration:	May 19, 2023
Publication (planned with CEUR-WS.org*)	August 2023
*Proceedings shall be submitted to CEUR-WS.org for online publication

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:	June 06, 2023
Publication (planned with CEUR-WS.org*)	August 2023
*Proceedings shall be submitted to CEUR-WS.org for online publication

Special track proposal
Proposal submission (contact):	Anytime before February 08 February 15 2023

Panel discussion
Panel Discussion proposals:	May 21, 2023
Notification of acceptance:	May 28, 2023
Registration of Panel Discussions facilitators:	June 06, 2023

Conference
The World Conference on eXplainable AI	26-28 July 2023

Submission

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 8 and 12 pages (including references)
extended abstracts	between 4 and 8 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 implication
s 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 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. They are not simply long abstracts or ‘work-in-progress’. 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.

Special track articles

The article submitted to the special tracks follows the normal procedure and must be submitted via easy-chair, as mentioned above. The types of articles admitted are full articles, shorter articles and extended abstracts, as described above. The author of an article for a special track must select the name of such special track in the list of topics in easy-chair, along with other relevant topics.

Anonymity for review

The submitted article (a .pdf) must be anonymous because 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.

Review process

The Peer-Review process

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.

According to the quality of the accepted article and its rank among all the other accepted manuscripts, it can be accepted for a full or short presentation or as a poster.

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

Publication

Proceedings publication

Each accepted and presented full, short and extended abstract manuscript, either as an oral presentation or as a poster, will be included in the conference proceedings by Springer in Communications in Computer and Information Science, edited by the general chair.  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.