XKDD 2023 - Call for Papers ------------------------------------------------------------------------- 5th ECML-PKDD International Workshop on eXplainable Knowledge Discovery in Data Mining ------------------------------------------------------------------------- Torino, 18th September 2023 http://xkdd2023.isti.cnr.it/ (co-located with the ECML-PKDD 2023 conference https://2023.ecmlpkdd.org/) CONTEXT & OBJECTIVES In the past decade, machine learning based decision systems have been widely used in a wide range of application domains, like credit score, insurance risk, and health monitoring, in which accuracy is of the utmost importance. Although the support of these systems has an immense potential to improve the decision in different fields, their use may present ethical and legal risks, such as codifying biases, jeopardizing transparency and privacy, and reducing accountability. Unfortunately, these risks arise in different applications. They are made even more serious and subtly by the opacity of recent decision support systems, which are often complex and their internal logic is usually inaccessible to humans. Nowadays, most Artificial Intelligence (AI) systems are based on Machine Learning algorithms. The relevance and need for ethics in AI are supported and highlighted by various initiatives arising from the researches to provide recommendations and guidelines in the direction of making AI-based decision systems explainable and compliant with legal and ethical issues. These include the EU's GDPR regulation which introduces, to some extent, a right for all individuals to obtain ``meaningful explanations of the logic involved'' when automated decision making takes place, the ``ACM Statement on Algorithmic Transparency and Accountability'', the Informatics Europe's ``European Recommendations on Machine-Learned Automated Decision Making'' and ``The ethics guidelines for trustworthy AI'' provided by the EU High-Level Expert Group on AI. The challenge to design and develop trustworthy AI-based decision systems is still open and requires a joint effort across technical, legal, sociological and ethical domains. The purpose of XKDD, eXaplaining Knowledge Discovery in Data Mining, is to encourage principled research that will lead to the advancement of explainable, transparent, ethical and fair data mining and machine learning. Also, this year the workshop will seek submissions addressing uncovered important issues in specific fields related to eXplainable AI (XAI), such as XAI for a more Social and Responsible AI, XAI as a tool to align AI with human values, XAI for Outlier and Anomaly Detection, quantitative and qualitative evaluation of XAI approaches, and XAI case studies. The workshop will seek top-quality submissions related to ethical, fair, explainable and transparent data mining and machine learning approaches. Papers should present research results in any of the topics of interest for the workshop, as well as tools and promising preliminary ideas. XKDD asks for contributions from researchers, academia and industries, working on topics addressing these challenges primarily from a technical point of view but also from a legal, ethical or sociological perspective. Topics of interest include, but are not limited to: TOPICS - XAI for Social AI - XAI for Responsible AI - XAI to Align AI with Human Values - XAI for Outlier and Anomaly Detection - Quantitative and Qualitative Evaluation of XAI approaches - Transparent-by Design Models - XAI Case studies - XAI for Privacy-Preserving Systems - XAI for Federated Learning - XAI for Time Series based Approaches - XAI for Graph based Approaches - XAI for Visualization - XAI in Human-Machine Interaction - XAI in Human-in-the-Loop Interactions - Counterfactual Explanations - Human-Model Interfaces for XAI approaches - Explainable Artificial Intelligence (XAI) - Interpretable Machine Learning - Transparent Data Mining - XAI for Fairness Checking - Explanation, Accountability and Liability from an Ethical and Legal Perspective Also, the XKDD 2023 edition is co-organized with DynXAI - Explainable Artificial Intelligence: From Static to Dynamic. SUBMISSION & PUBLICATION All contributions will be reviewed by at least three members of the Program Committee. As regards size, contributions can be up to is 14 pages references excluded in LNCS format, i.e., the ECML PKDD 2023 submission format. All papers should be written in English. The following kinds of submissions will be considered: research papers, tool papers, case study papers and position papers. Detailed information on the submission procedure is available at the workshop web page: http://xkdd2023.isti.cnr.it/ If pandemic conditions permit, the workshop will be held in an on-site format. Recordings of the lectures will be made available to all registered participants. Accepted papers will be published after the workshop by Springer in a volume of Lecture Notes in Computer Science (LNCS). The condition for inclusion in the post-proceedings is that at least one of the co-authors registered to ECML-PKDD and presented the paper at the workshop. Pre-proceedings will be available online before the workshop. We also allow accepted papers to be presented without publication in the conference proceedings if the authors choose to do so. Some of the full paper submissions may be accepted as short papers after review by the Program Committee. A special issue of a relevant international journal with extended versions of selected papers is under consideration. The submission link is: https://cmt3.research.microsoft.com/ECMLPKDDworkshop2023/Track/16/Submission... The XKDD 2023 workshop is co-organized and will be held jointly the DynXAI 2023 workshop, please visit https://sites.google.com/view/dynxai-ecmlpkdd-2023/ for the detailed CfP. IMPORTANT DATES Paper Submission deadline: June 20, 2023 Accept/Reject Notification: July 13, 2023 Camera-ready deadline: July 31, 2023 Workshop: September 18, 2023 PROGRAM CO-CHAIRS * Przemyslaw Biecek, Warsaw University of Technology, Poland * Tania Cerquitelli, Politecnico di Torino, Torino, Italy * Riccardo Guidotti, University of Pisa, Italy * Francesca Naretto, Scuola Normale Superiore, Pisa, Italy * Daniele Regoli, Instesa Sanpaolo, Italy PROGRAM COMMITEE * Miguel Couceiro, Università de Lorraine, France * Alessandro Castelnovo, Intesa Sanpaolo, Italy * Riccardo Crupi, Intesa Sanpaolo, Italy * Josep Domingo-Ferrer, Universitat Rovira i Virgili, Spain * Françoise Fessant, Orange Labs, France * Salvatore Greco, Politecnico di Torino, Italy * Andreas Holzinger, Medical University of Graz, Austria * Paulo Lisboa, Liverpool John Moores University, UK * Marcin Luckner, Warsaw University of Technology, Poland * Amedeo Napoli, CNRS, France * John Mollas, Aristotle University of Thessaloniki, Greece * Enea Parimbelli, University of Pavia, Italy * Francesca Pratesi, ISTI-CNR, Italy * Roberto Prevete, University of Napoli, Italy * Antonio Rago, Imperial College London, UK * Eliana Pastor, Politecnico di Torino, Italy * Jan Ramon, Inria, France * Xavier Renard, AXA, France * Mahtab Sarvmaili, Dalhousie University, Canada * Udo Schlegel, Konstanz University, Germany * Mattia Setzu, University of Pisa, Italy * Dominik Slezak, University of Warsaw, Poland * Myra Spiliopoulou, University Magdeburg, Germany * Francesco Spinnato, Scuola Normale Superiore, Italy * Grigorios Tsoumakas, Aristotle University of Thessaloniki, Greece * Genoveva Vargas-Solar, CNRS, LIRIS, France * Albrecht Zimmermann, Università de Caen, France INVITED SPEAKERS * Andreas Theissler, Aalen University of Applied Sciences, Aalen, Germany * Dino Pedreschi, University of Pisa, Italy (tentative) STEERING COMMITTEE * Riccardo Guidotti, University of Pisa * Anna Monreale, University of Pisa * Salvatore Rinzivillo, ISTI-CNR, Pisa CONTACT All inquiries should be sent to francesca.naretto@di.unipi.it, riccardo.guidotti@unipi.it Prima di stampare, pensa all'ambiente ** Think about the environment before printing ________________________________ Il presente messaggio, inclusi gli eventuali allegati, ha natura aziendale e potrebbe contenere informazioni confidenziali e/o riservate. 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