XKDD and Beyond 2025 - Call for Papers
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7th ECML PKDD International Workshop on eXplainable Knowledge Discovery in Data Mining and UNLEARNING
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co-located with the ECML-PKDD 2025 Conference

Workshop website: https://xkdd2025.isti.cnr.it/


IMPORTANT DATES
Paper Submission deadline: June 6th, 2025
Accept/Reject Notification: July 14th, 2025
Camera-ready deadline: TBD
Workshop: September 19th, 2025


CONTEXT & OBJECTIVES
In recent years, Artificial Intelligence (AI)-driven decision systems have been widely applied in domains such as credit scoring, insurance risk assessment, and health monitoring, where predictive accuracy is critical. While these systems enhance decision-making capabilities, they also introduce ethical and legal challenges, including bias reinforcement, reduced transparency, privacy concerns, and diminished accountability. The complexity and opacity of modern AI models further exacerbate these risks, making it difficult to ensure fairness and compliance with ethical and legal standards.
Most AI systems today rely on Machine Learning algorithms, and the need for ethics and trust in AI has been emphasized through various regulations and guidelines. Frameworks such as the EU’s GDPR mandate the right to "meaningful explanations" of automated decision-making processes, while the AI Act advocates for explainability, transparency, and accountability in AI-driven decision-making.

Despite these efforts, the challenge of developing AI systems that are truly explainable, trustworthy, and compliant with regulatory frameworks remains unresolved. Many methodologies address aspects such as explainability, fairness, and accountability, but a comprehensive and effective framework is still lacking. Addressing these challenges requires collaboration across disciplines, including computer science, law, sociology, and ethics.

XKDD and Beyond is dedicated to advancing research on explainable, transparent, ethical, and fair AI-driven decision systems. This year, the workshop expands its scope to include the emerging topic of **unlearning** and its intersection with explainable AI (XAI). Unlearning, or the removal of specific knowledge from AI models, is a crucial challenge, especially in contexts requiring compliance with the "right to be forgotten" under GDPR. However, achieving effective unlearning is complex, as model parameters encode learned information in intricate ways.

XAI techniques play a vital role in operationalizing unlearning by providing insights into how decisions are made, identifying the influence of specific data points, and guiding targeted interventions to remove unwanted knowledge while preserving model performance. By integrating explainability with unlearning, AI systems can become more adaptable, accountable, and aligned with ethical and legal standards.

XKDD 2025 invites researchers and practitioners from academia and industry to explore the latest advancements in explainability, trust, and unlearning in AI. Join us in shaping the future of ethical and transparent AI-driven decision-making.
Topics of interest include, but are not limited to:

- XAI and Unlearning
- XAI for Trustworthy AI
- XAI for Social AI
- XAI to Align AI with Human Values
- XAI for Outlier and Anomaly Detection
- XAI methodologies for tabular data, images, text, and time series
- XAI for ethical, fair, and transparent AI systems
- XAI techniques for enabling unlearning in machine learning models
- The role of XAI in privacy preservation and regulatory compliance
- Case studies on XAI and unlearning applications in real-world scenarios
- Quantitative and qualitative evaluations of XAI
- Quantitative and qualitative evaluations of unlearning approaches
- XAI for Federated Learning
- XAI for Graph-based Approaches
- XAI for Visualization
- Interpretable Machine Learning
- Transparent Data Mining
- XAI for Fairness Checking
- Multi-level XAI
- Explanation, Accountability, and Liability from an Ethical and Legal Perspective

XKDD 2025 is a Workshop of the ECML-PKDD Conference: https://ecmlpkdd.org/2025/


SUBMISSION & PUBLICATION
The submission link is: https://cmt3.research.microsoft.com/ECMLPKDDWorkshopTrack2025/

Papers must be written in English and formatted according to the Springer Lecture Notes in Computer Science (LNCS) guidelines following the style of the main conference.
The maximum length of either research or position papers is 16 pages references included. Overlength papers will be rejected without review (papers with smaller page margins and font sizes than specified in the author instructions and set in the style files will also be treated as overlength).
Authors who submit their work to XKDD 2025 commit themselves to present their paper at the workshop in case of acceptance. XKDD 2025 considers the author list submitted with the paper as final. No additions or deletions to this list may be made after paper submission, either during the review period, or in case of acceptance, at the final camera ready stage.
Condition for inclusion in the post-proceedings is that at least one of the co-authors has presented the paper at the workshop. Pre-proceedings will be available online before the workshop.
All accepted papers will be published as post-proceedings in LNCSI and included in the series name Lecture Notes in Computer Science.

More info at: https://xkdd2025.isti.cnr.it/


PROGRAM CO-CHAIRS
- Francesca Naretto, University of Pisa, Pisa, Italy
- Francesco Spinnato, University of Pisa, Pisa, Italy
- Przemyslaw Biecek, Warsaw University of Technology, Poland
- Josep Domingo-Ferrer, Universitat Rovira i Virgili, Catalonia


INVITED SPEAKERS
- Andreas Theissler, Aalen University of Applied Sciences, Germany
- Josep Domingo-Ferrer, Universitat Rovira i Virgili, Catalonia


STEERING COMMITTEE
- Riccardo Guidotti, University of Pisa, Italy
- Anna Monreale, University of Pisa, Italy
- Salvatore Rinzivillo, ISTI-CNR, Pisa, Italy
- Przemyslaw Biecek, Warsaw University of Technology, Warsaw, Poland


VENUE
The event will take place at the ECML-PKDD 2025 Conference.


CONTACT
All inquires should be sent to francesca.naretto@unipi.it, francesco.spinnato@di.unipi.it