The 1st International Conference on eXplainable Artificial Intelligence

xAI 2023

Artificial Intelligence

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 GraphsAgent-based explainable systemsAnte-hoc approaches for interpretabilityArgumentative-based approaches for xAIArgumentation theory for xAIAttention mechanisms for xAIAutomata for explaining RNN modelsAuto-encoders & latent spaces explainabilityBayesian modelling for interpretabilityBlack-boxes vs white-boxesCase-based explanations for AI systemsCausal inference & explanationsConstraints-based explanationsDecomposition of NNET-models for XAIDeep learning & XAI methodsDefeasible reasoning for explainabilityEvaluation approaches for XAI-based systemsExplainable methods for edge computingExpert systems for explainabilityExplainability & the semantic webExplainability of signal processing methodsFinite state machines for explainabilityFuzzy systems & logic for explainabilityGraph neural networks for explainabilityHybrid & transparent black box modellingInterpreting & explaining CNN NetworksInterpretable representational learningMethods for latent spaces interpretationsModel-specific vs model-agnostic methodsNeuro-symbolic reasoning for XAINatural language processing for explanationsOntologies & taxonomies for supporting XAIPruning methods with XAIPost-hoc methods for explainabilityReinforcement learning for enhancing XAIReasoning under uncertainty for explanationsRule-based XAI systemsRobotics & explainabilitySample-centric & Dataset-centric explanationsSelf-explainable methods for XAISentence embeddings to xAI semantic featuresTransparent & explainable learning methodsUser interfaces for explainabilityVisual methods for representational learningXAI BenchmarkingXAI methods for neuroimaging & neural signalsXAI & reservoir computing

Ethical considerations for XAI

Accountability & responsibility in XAIAddressing user-centric requirements for XAITrade-off model accuracy & interpretabilityExplainable Bias & fairness of XAI systemsExplainability for discovering, improving, controlling & justifyingExplainability as prerequisite for responsible AIExplainability & data fusionExplainability/responsibility in policy guidelinesExplainability pitfalls & dark patterns in XAIHistorical foundations of XAIMoral principles & dilemma for XAIMultimodal XAI approachesPhilosophical consideration of synthetic explanationsPrevention/detection of deceptive AI explanationsSocial implications of synthetic explanationsTheoretical foundations of XAITrust & explainable AIThe logic of scientific explanation for/in AIExpected epistemic & moral goods for XAIXAI for fairness checkingXAI for time series-based approaches

Psychological notions & concepts for XAI

Algorithmic transparency & actionabilityCognitive approaches for explanationsCognitive relief in explanationsContrastive nature of explanationsComprehensibility vs interpretabilityCounterfactual explanationsDesigning new explanation stylesExplanations for correctabilityFaithfulness & intelligibility of explanationsInterpretability vs traceabilityexplanations Interestingness & informativenessIrrelevance of probabilities to explanationsIterative dialogue explanationsJustification & explanations in AI systemsLocal vs global interpretability & explainabilityMethods for assessing explanations qualityNon-technical explanations in AI systemsNotions and metrics of/for explainabilityPersuasiveness & robustness of explanationsPsychometrics of human explanationsQualitative approaches for explainabilityQuestionnaires & surveys for explainabilityScrutability & diagnosis of XAI methodsSoundness & stability of XAI methods

Social examinations of XAI

Adaptive explainable systemsBackwards & forward-looking responsibility forms to XAIData provenance & explainabilityExplainability for reputationEpistemic and non-epistemic values for XAIHuman-centric explainable AIPerson-specific XAI systemsPresentation & personalization of AI explanations for target groupsSocial nature of explanations

Legal & administrative considerations of/for XAI

Black-box model auditing & explanationExplainability in regulatory complianceHuman rights for explanations in AI systemsPolicy-based systems of explanationsThe potential harm of explainability in AITrustworthiness of XAI for clinicians/patientsXAI methods for model governanceXAI in policy developmentXAI for situational awareness/compliance behavior

Safety & security approaches for XAI

Adversarial attacks explanationsExplanations for risk assessmentExplainability of federated learningExplainable IoT malware detectionPrivacy & agency of explanationsXAI for Privacy-Preserving SystemsXAI techniques of stealing attack & defenceXAI for human-AI cooperationXAI & models output confidence estimation

Applications of XAI-based systems

Application of XAI in cognitive computingDialogue systems for enhancing explainability

Explainable methods for medical diagnosisBusiness & MarketingBiomedical knowledge discovery & explainabilityExplainable methods for HCIExplainability in decision-support systemsExplainable recommender systemsExplainable methods for finance & automatic trading systemsExplainability in agricultural AI-based methodsExplainability in transportation systemsExplainability for unmanned aerial vehiclesExplainability in brain-computer interfacesInteractive applications for XAIManufacturing chains & application of XAIModels of explanations in criminology, cybersecurity & defenceXAI approaches in Industry 4.0XAI systems for health-careXAI technologies for autonomous drivingXAI methods for bioinformaticsXAI methods for linguistics/machine translationXAI methods for neuroscienceXAI models & applications for IoTXAI methods for XAI for terrestrial, atmospheric, & ocean remote sensingXAI in sustainable finance & climate financeXAI in bio-signals analysis

Important dates

Special track (workshop) proposal

Proposal submission:February 08, 2023Notification of acceptance:February 15, 2023

Article submission

Abstracts registration deadline (easy-chair):April 15, 2023Article submission deadline (easy-chair):April 20, 2023Notification of acceptance:May 12, 2023Registration & camera ready:May 19, 2023

Doctoral consortium submission

Proposal registration deadline (easy-chair):April 16th 2023Proposal submission deadline (easy-chair):April 30, 2023Notification of acceptance:May 7, 2023Registration:May 19, 2023

Late-breaking work & demos

Late-breaking work & demo registration (easy-chair):May 21, 2023Late-breaking work & demo submission (easy-chair):May 28, 2023Notification of acceptance:May 06, 2023

Panel discussion

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


The World Conference on eXplainable AI26-28 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 articlesbetween 12 and 24 pages (including references)short articlesbetween 6 and 12 pages (including references)extended abstractsbetween 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.

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, the 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


Proceedings publication

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.