*** Apologies for cross-postings ***

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Call For Papers
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2nd Workshop on Machine Unlearning and Privacy Preservation (WIPE-OUT 2) to be held as
part of the European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECML-PKDD 2026).

Workshop proceedings will be published as indexed post-proceedings
volume of Springer's Communications in Computer and Information Science (CCIS).

Date: September 7th, 2026 - Naples (Italy)
Full Information at: https://aiimlab.org/events/ECML_PKDD_2026_WIPE-OUT_2_Workshop_on_Machine_Unlearning_and_Privacy_Preservation
Submission System:
https://cmt3.research.microsoft.com/ECMLPKDDWT2026/Submission/Manage
(Select “WIPE-OUT 2 Workshop on Machine Unlearning and Privacy Preservation”)

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Important Dates
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Paper Submissions deadline: June 5th, 2026
Notifications: July 5th, 2026
Camera-Ready: July 10th, 2026
Workshop: September 7th, 2026

All deadlines are 11:59 pm AoE.

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Workshop Aims and Scope
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Can AI learn to forget? It must.

With GDPR, the AI Act, and growing demands for ethical AI, Machine Unlearning is one of the most critical challenges in the field right now, enabling the selective removal of learned information without costly retraining, mitigating biases, protecting sensitive data, and aligning AI with evolving regulatory standards.

Building on the success of the first edition at ECML-PKDD 2025 in Porto (~50 participants,
keynotes by Google DeepMind researchers), WIPE-OUT 2 brings together pioneers in MU
to push boundaries in privacy-preserving AI. From cutting-edge algorithms to real-world
applications, we foster collaboration to tackle the legal, technical, and ethical
challenges of unlearning. Join us in redefining the AI landscape!

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Workshop Keywords
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Machine Unlearning · Privacy Preservation · Security and Privacy · Ethical AI · Model Editing

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Workshop Topics
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WIPE-OUT 2 welcomes contributions on all topics related to Machine Unlearning
across domains (e.g., finance, business, basic sciences, construction, computational
advertising, medical, etc.) and independent of data types (e.g., networks, tabular,
unstructured, graphs, logs, spatiotemporal, multimedia, time series, genomic
sequences, and streaming data). Contributions can also include research or
perspectives regarding the following:

* Foundations and Theory of Machine Unlearning:
– Theoretical foundations, guarantees, and bounds for Machine Unlearning
– Certified and approximate unlearning: complexity, information-theoretic limits, and impossibility results
– Development of new Machine Unlearning and Model Editing Algorithms
– Connections between unlearning, influence functions, and data attribution

* Unlearning in LLMs and Foundation Models:
– Knowledge Removal and Model Editing in Large Language Models
– Unlearning unsafe, copyrighted, or private content from pretrained LLMs
– Concept Erasure and Safety Alignment via Unlearning in Generative Models
– Unlearning in Multimodal Foundation Models (Vision-Language, Text-to-Image)

* Machine Unlearning in Computer Vision:
– Class-Level and Instance-Level Forgetting in Image Classifiers and Object Detectors
– Concept Erasure in Diffusion Models and GANs
– Unlearning for Face Recognition, De-Identification, and Visual Privacy
– Continual and Incremental Unlearning in Vision Systems

* Machine Unlearning in Recommender Systems:
– User Data Removal in Collaborative Filtering and Content-Based Recommenders
– Unlearning in Sequential, Session-Based, and Graph-Based Recommendation
– Impact of Unlearning on Recommendation Quality and Fairness
– Right-to-be-Forgotten Compliance in Deployed Recommendation Platforms

* Privacy and Compliance in Machine Unlearning:
– Privacy-Preserving Techniques for Machine Learning Systems
– Interactions Between Differential Privacy, Federated Learning, and Unlearning
– Federated and Decentralized Unlearning Processes
– Membership Inference, Data Auditing, and Privacy Verification After Unlearning
– Regulatory Compliance (GDPR, AI Act, CCPA) and the Right to Be Forgotten

* Systems and Scalable Machine Unlearning:
– Efficient and Scalable Machine Unlearning in Big Data Systems
– Real-Time and Streaming Data Unlearning Systems
– Systems-Level Design for Unlearning-Ready ML Pipelines
– Hardware-Aware and Resource-Efficient Unlearning Methods

* Evaluation and benchmarking:
– Evaluation Metrics for quantifying unlearning performance
– Development of new Evaluation protocols for Machine Unlearning
– Tools and Benchmarks for Unlearning Frameworks
– Robustness and verification of unlearning and editing guarantees

* Implications of Machine Unlearning:
– Unlearning for Explainable and Interpretable AI
– Ethical, legal, and societal aspects of Machine Unlearning
– Machine Unlearning in High-Stakes Applications (e.g., healthcare, finance, ...)

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Submission and Publication
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Papers must be submitted electronically via the Microsoft CMT system.
Submission system:
https://cmt3.research.microsoft.com/ECMLPKDDWT2026/Submission/Manage
From the submission system, select the “WIPE-OUT 2 Workshop on Machine Unlearning and Privacy Preservation” track to create a new submission.
We invite authors to submit unpublished, original papers written in English.
Submitted papers should not have been previously published or accepted for
publication in a substantially similar form in any peer-reviewed venue, such as journals,
conferences, or workshops. Authors should use the Springer LNCS proceedings template
(LaTeX or Word). We will consider three different submission types:

Full papers (up to 14 pages) should clearly describe the state of the art and state the
proposal's contribution in the application domain, even if presenting preliminary
results. In particular, research papers should describe the methodology in detail,
experiments should be repeatable, and a comparison with existing approaches in
the literature should be made.

Reproducibility/Replicability papers (up to 14 pages) should repeat prior
experiments using the source code and datasets to show how, why, and when
the methods work or not (replicability), or repeat prior experiments in new contexts
(e.g., different domains, datasets, evaluation metrics) to generalize and validate
previous work (reproducibility).

Short, Demo, and Position papers (up to 8 pages) should introduce new points of
view on the workshop topics or summarize a group's experience in the field. Practice
and experience reports must detail real-world scenarios in which Machine Unlearning
is needed.

Submissions should not exceed the indicated pages, including any diagrams and
references. All submissions will undergo a double-blind review process and be
reviewed by at least three reviewers based on relevance, novelty/originality,
significance, technical quality, clarity of presentation, quality of references,
and reproducibility.

Submitted papers will be rejected without review if they are not correctly anonymized,
do not comply with the template or follow the above guidelines.

Publication: Accepted workshop papers will be included in an indexed post-workshop
proceedings volume published by Springer Communications in Computer and
Information Science (CCIS), indexed on Google Scholar, DBLP, and Scopus.
The authors of selected papers may be invited to submit an extended version
to a special journal issue.

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Registration and Presentation Policy
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Please be aware that at least one author per paper must register and attend the
workshop to present the work. We expect the authors, the program committee,
and the organizing committee to adhere to the ECML-PKDD Code of Conduct.

The Main Conference organization team will manage the registration:
https://ecmlpkdd.org/2026/

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Workshop Chairs
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Andrea D'Angelo - University of L'Aquila (andrea.dangelo6@graduate.univaq.it)
Claudio Savelli - Polytechnic of Turin (claudio.savelli@polito.it)
Flavio Giobergia - Polytechnic of Turin (flavio.giobergia@polito.it)
Francesco Gullo - University of L'Aquila (gullof@acm.org)
Giovanni Stilo - Luiss University of Rome (gstilo@luiss.it)

For general inquiries, please contact the chairs listed above.