The 5-volume set CCIS 2839 – 2843 constitutes the refereed proceedings of several workshops held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2025, which took place in Porto, Portugal, in September 2025.
The 236 full papers included in these proceedings were carefully reviewed and selected from 413 submissions. The papers were organized topical sections as follows:
Part I: Workshop on Data Science for Social Good SoGood 2025), Workshop on Bias and Fairness in AI (BIAS 2025), Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2025), Human-Centered Data Mining Workshop (HuMine 2025) and Workshop on Data-Centric Artificial Intelligence (DEARING 2025).
Part II: Workshop on Hybrid Human-Machine Learning and Decision Making (HLDM 2025), Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2025), Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2025),Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2025), Workshop on Deep Learning Meets Neuromorphic Hardware (DLmNH 2025), Machine Learning for Cybersecurity (MLCS 2025),AI for Safety-Critical Infrastructures (AI-SCI 2025) and Workshop on Innovations, Privacy-preservation, and Evaluations of Machine Unlearning Techniques (WIPE-OUT).
Part III: Workshop on Machine Learning for Sustainable Power Systems (ML4SPS 2025), Workshop on Synthetic Data for AI Trustworthiness and Evolution (SynDAiTE 2025), Workshop on MIning Data for Financial Applications (MIDAS 2025), Workshop on Advancements in Federated Learning (WAFL 2025) and Workshop on Mining and Learning with Graphs (MLG 2025).
Part IV: Workshop on Interactive Adaptive Learning (IAL 2025), Workshop on Machine Learning for Irregular Time Series (ML4ITS 2025), Interactive eXplainable AI, Theory and Practice (IXAIT 2025), Workshop on Learning on Real and Synthetic Medical Time Series Data (MED-TIME 2025), Workshop on Responsible Healthcare Using Machine Learning (RHCML 2025), Workshop for Explainable AI in Time Series and Data Streams (TempXAI 2025) and Workshop on Explainable Knowledge Discovery in Data Mining and Unlearning (XKDD 2025).
Part V: Workshop on Learning from Small Data (LFSD 2025), Workshop on Machine Learning for Earth Observation (MACLEAN 2025), Workshop on Artificial Intelligence, Data Analytics and Democracy (AIDEM 2025) and Discovery Challenges.
Table of Contents:
.- Workshop on Interactive Adaptive Learning (IAL 2025).
.- Low Query Budget Active Learning for Classification and Regression.
.- Adaptable Hindsight Experience Replay for Search-Based Learning.
.- When Intrinsic Motivation Fails: Exploration Challenges in Decentralized MARL.
.- Trustworthy Active Learning through Reputation and Weighted Voting Mechanisms.
.- Workshop on Machine Learning for Irregular Time Series (ML4ITS 2025).
.- Closing the Gap Between Synthetic and Ground Truth Time Series Distributions via Neural Mapping.
.- Demonstration of a Universal Algorithm for Satellite Anomaly Detection in Spacecraft Anomaly Challenge.
.- A Hierarchical Ensemble Pipeline for Anomaly Detection in ESA Satellite Telemetry.
.- Morphological Leave-One-Out Kernel Density Estimates for Anomaly Detection in Satellite Telemetry.
.- Interactive eXplainable AI, Theory and Practice (IXAIT 2025).
.- Can SHAP-based explanations differentiate between concept drift and scale drift in computer networks data?.
.- Aligning AI Explanations with User Needs: A Qualitative Study of XAI Methods.
.- SepsisVision: Web-Based Support Tool for Sepsis Mortality Risk Screening through Explanatory and Exploratory User Interfaces.
.- Explainable Visual Anomaly Detection with Multimodal Models and Metadata-Augmented Prompts.
.- Workshop on Learning on Real and Synthetic Medical Time Series Data (MED-TIME 2025).
.- Improved Sleep Stage Tagging on Wearables via Knowledge Distillation.
.- Federated Markov Imputation: Privacy-Preserving Temporal Imputation in Multi-Centric ICU Environments.
.- Alignment of Multiple Item Set Sequences for Apnea Detection.
.- On the role of prognostic factors and effect modifiers in unraveling population heterogeneity.
.- Workshop on Responsible Healthcare Using Machine Learning (RHCML 2025).
.- MedFusion-LM: Explainable Large Language Model for Transforming Medical Outcomes in Federated Learning with Neural Architecture Search Blueprints.
.- A Comparative Study on the Responsible Use of Public LLMs for Self-Diagnosis.
.- Voices Between Lines: Interpretable Labeling of Mental Health Minority Topics with Seed Guidance and LLMs.
.- A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption.
.- Positive-Unlabeled Learning for User-Centred XAI: a Case Study in Healthcare.
.- Challenges in Explaining Pretrained Clinical Text Classifiers.
.- Privacy-Preserving AI-based Glaucoma Referral using Multi-Centric Real-World Data: A Feasibility study.
.- Gender Prediction from Polish Ethnicity Fundus Images using Foundation Model.
.- Characterizing Publicly Available Tabular Health Data Sets for Responsible Machine Learning.
.- Workshop for Explainable AI in Time Series and Data Streams (TempXAI 2025).
.- Towards Explainable Deep Clustering for Time Series Data.
.- Why Do Class-Dependent Evaluation Effects Occur with Time Series Feature Attributions? A Synthetic Data Investigation.
.- Causal Explanation of Concept Drift – A Truly Actionable Approach.
.- Explaining Concept Drift through the Evolution of Group Counterfactual Explanations.
.- Transparent and Adaptive Pruning of Hoeffding Trees.
.- Proposing multi-perspective approach for detecting and explaining concepts drifts in evolving data.
.- An Empirical Evaluation of Factors Affecting SHAP Explanation of Time Series Classification.
.- A Real-Time Pipeline for Anomaly Detection and Explanation in Streaming Data.
.- Workshop on Explainable Knowledge Discovery in Data Mining and Unlearning (XKDD 2025).
.- Concept-AIME: A Dual Inverse-Model Framework for Concept-Level Global and Local Explanations of Black-Box Predictors.
.- TT-XAI: Trustworthy Clinical Text Explanations via Keyword Distillation and LLM Reasoning.
.- From Explainable AI to Model Diagnosis: A Framework and Comparative Study of Human and ML-Based Explanation Diagnosis.
.- KL-Guided Concept-Based Learning for Explainable Classification.
.- Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainability.
.- Rule vs. SHAP: Complementary Tools for Understanding and Verifying ML Models.
.- A Concept-based approach to Voice Disorder Detection.
.- What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests.
.- The Right to be Forgotten in the Age of AI: Legal, Philosophical, and Technical Challenges.
.- Rotation‑ and Scale-Invariant Shape Extraction from Vessel Trajectories for Human‑In‑The‑Loop Monitoring: a case study at the Ports of Brittany.
