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XKDD 2022 : 4th International Workshop on eXplainable Knowledge Discovery in Data Mining


When Sep 19, 2022 - Sep 19, 2022
Where Grenoble
Submission Deadline Jul 4, 2022
Notification Due Jul 20, 2022
Final Version Due Jul 31, 2022
Categories    explainability   interpretable machine learning   transparent data mining   ethics, fairness and privacy

Call For Papers

XKDD 2022 - Call for Papers
4th International Workshop on eXplainable Knowledge Discovery in Data Mining

Due to the many requests received we decided to extend the submission to July 4, 2022.

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, eXplainable 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.
The workshop will seek top-quality submissions related to ethical, fair, explainable and transparent data mining and machine learning approaches.
Also, this year the workshop will seek submissions addressing uncovered important issues in specific fields related to eXplainable AI (XAI), such as privacy and fairness, application in real case studies, benchmarking, explanation of decision systems based on time series and graphs which are becoming more and more important in nowadays applications.
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:

- Explainable Artificial Intelligence (XAI)
- Interpretable Machine Learning
- Transparent Data Mining
- XAI for Fairness Checking approaches
- 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 Benchmarking
- XAI Case studies
- Counterfactual Explanations
- Ethics Discovery for Explainable AI
- Privacy-Preserving Explanations
- Transparent Classification Approaches
- Explanation, Accountability and Liability from an Ethical and Legal Perspective
- Iterative Dialogue Explanations
- Explanatory Model Analysis
- Human-Model Interfaces
- Human-Centered Artificial Intelligence
- Human-in-the-Loop Interactions
- XAI Case Studies and Applications

All contributions will be reviewed by at least three members of the Program Committee. As regards size, contributions can be up to 16 pages in LNCS format, i.e., the ECML PKDD 2022 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:

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:

Paper Submission deadline: June 20, 2022
Accept/Reject Notification: July 13, 2022
Camera-ready deadline: July 31, 2022
Workshop: September 19, 2022

* Przemyslaw Biecek, Warsaw University of Technology, Poland
* Riccardo Guidotti, University of Pisa, Italy
* Francesca Naretto, Scuola Normale Superiore, Pisa, Italy
* Andreas Theissler, Aalen University of Applied Sciences, Aalen, Germany

* Leila Amgoud, CNRS, France
* Francesco Bodria, Scuola Normale Superiore, Italy
* Umang Bhatt, University of Cambridge, UK
* Miguel Couceiro, INRIA, France
* Menna El-Assady, AI Center of ETH, Switzerland
* Josep Domingo-Ferrer, Universitat Rovira i Virgili, Spain
* Françoise Fessant, Orange Labs, France
* Andreas Holzinger, Medical University of Graz, Austria
* Thibault Laugel, AXA, France
* Paulo Lisboa, Liverpool John Moores University, UK
* Marcin Luckner, Warsaw University of Technology, Poland
* John Mollas, Aristotle University of Thessaloniki, Greece
* Ramaravind Kommiya Mothilal, Everwell Health Solutions, India
* Amedeo Napoli, CNRS, France
* Roberto Prevete, University of Napoli, Italy
* Antonio Rago, Imperial College London, UK
* Jan Ramon, INFRIA, France
* Xavier Renard, AXA, France
* Mahtab Sarvmaili, Dalhousie University, Canada
* Christin Seifert, University of Duisburg-Essen, Germany
* Udo Schlegel, Konstanz University, Germany
* Mattia Setzu, University of Pisa, Italy
* Dominik Slezak, University of Warsaw, Poland
* Fabrizio Silvestri, Università di Roma, Italy
* Francesco Spinnato, Scuola Normale Superiore, Italy
* Vicenc Torra, Umea University, Sweden
* Cagatay Turkay, University of Warwick, UK
* Marco Virgolin, Chalmers University of Technology, Netherlands
* Martin Jullum, Norwegian Computing Center, Norway
* Albrecht Zimmermann, Université de Caen, France
* Guangyi Zhang, KTH Royal Institute of Technology, Sweden

* Prof. Wojciech Samek, TU Berlin
* Prof. Anna Monreale, University of Pisa

* Riccardo Guidotti, University of Pisa
* Anna Monreale, University of Pisa
* Salvatore Rinzivillo, ISTI-CNR, Pisa

ECML-PKDD 2022 plans a hybrid organization for workshops.
Therefore a person can attend an online event as long as she/he registers for the conference by using the video conference registration fee:
Please note the video conference registration fee also allows you to follow the main conference. However, for an in-person event, interactions and discussions are much easier face-to-face.
Thus, we believe that it is important that speakers attend in-person workshops to get fruitful events, and we highly encourage authors of submitted papers to plan to participate on-site at the event.

All inquiries should be sent to

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