HLDM'26

The Fourth Workshop on
Hybrid Human-Machine Learning and Decision Making

ECMLPKDD Workshop

September 7 2026

Naples, Italy

Overview

In the past, machine learning and decision-making have been treated as independent research areas. However, with the increasing emphasis on human-centered AI, there has been a growing interest in the understanding of how these two research fields interplay and can be jointly addressed to propose novel technological solutions having humans at their center. Recently, indeed, scholars started exploring how to enable the synergistic cooperation of humans and machines by conceiving hybrid approaches that aim to complement human decision-making rather than replace it, as well as strategies that leverage machine predictions to improve overall decision-making performance.

Despite these advances, we believe that our understanding of this topic is still in its infancy and that there is much to be learned about the interplay between human and Artificial Intelligence. To facilitate this exploration, there is a need for interdisciplinary events where researchers from multiple fields can come together to share their workflows, perspectives and insights.

The goal of our workshop is to bring together researchers with diverse backgrounds and expertise to explore effective hybrid machine learning and decision making. This will include approaches that explicitly consider the human-in-the-loop and the downstream goals of the human-machine system, as well as decision making strategies and HCI principles that promote rich and diverse interactions between humans and machines. Additionally, cognitive and legal aspects will be considered to identify potential pitfalls and ensure that trustworthy and ethical hybrid decision-making systems are developed.


Key Dates

  • Paper Submission Deadline: 7 June 2026 15 June 2026

  • Paper Author Notification: 7 July 2026 11 July 2026

  • Workshop date: 7 September 2026

Program Schedule

Initial Greetings (09:00 - 09:15)

Room TBD

Invited Talk: Eyke Hüllermeier (09:15 - 10:00)


Room TBD

Paper Presentations (10:00 - 10:30)

Bounded-Abstention Multi-horizon Time-series Forecasting
Robustness under severe distributional shift: a human-machine comparison
Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Models
Room TBD

Coffee Break (10:30 - 11:00)


Invited Talk: Adam Sanborn (11:00 - 11:45)

Title: Human Categorisation
Room TBD

Paper Presentations (11:45 - 12:45)

BRIDGET: Bridging Learning Together and Learning to Defer in Human--AI Collaboration
Understanding Operator Attitudes Toward AI-Supported Decision Making in Maritime Operations
Should I Stay or Should I Show? Learning to Selectively Disclose Information
What’s in the Bottle? A Survey and Roadmap of Concept Bottleneck Models
Integrating Multimodal Learning and Explainable AI for Enhanced and Interpretable Prostate Lesion Classification
Unlocking Serendipity in Sequential Recommendations
Room TBD

Lunch Break (13:00 - 14:00)

Invited Talk: Carina Dantas (14:00 - 14:45)

Title: Participatory co-research for ethical excellence in AI-based innovation
Room TBD

Paper Presentations (14:45 - 15:05)

ActivationReasoning: Logical Reasoning in Latent Activation Spaces
From Multimodal Observation to Interpretable Suggestions: Counterfactual Time-Expanded Relational Modeling of Surgical Teams
Room TBD

Poster Session (15:05 - 16:00)

Room TBD
Invited Speakers
Portrait of Prof. Eyke Hüllermeier
Prof. Eyke Hüllermeier

Eyke Hüllermeier is a full professor at the Institute of Informatics at LMU Munich, Germany, where he holds the Chair of Artificial Intelligence and Machine Learning. He studied mathematics and business computing, received his PhD in Computer Science from Paderborn University in 1997, and a Habilitation degree in 2002. Before joining LMU, he held professorships at Dortmund, Magdeburg, Marburg, and Paderborn, and spent two years as a Marie Curie fellow at IRIT in Toulouse, France.

His research focuses on machine learning, preference modelling, and reasoning under uncertainty. He has published more than 400 articles and serves as Editor-in-Chief of Data Mining and Knowledge Discovery and Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence. He is a Fellow of ELLIS and IFSA, President of EuADS, a member of the MCML Strategy Board, and a member of the relAI Steering Committee.

Portrait of Prof. Adam Sanborn
Prof. Adam Sanborn

Adam Sanborn is a Professor of Psychology at the University of Warwick. He gained his PhD in Psychological and Brain Sciences and Cognitive Science at Indiana University and completed postdoctoral work at the Gatsby Computational Neuroscience Unit at UCL. His research examines the rationality of human behaviour through Bayesian models, approximations to Bayesian models, and behavioural experiments.

His work has appeared in leading psychology journals and received best paper awards in psychology and computer science. His research has been funded by the ESRC, ERC, Alan Turing Institute, and NIESR. He previously served as Associate Editor of the Journal of Experimental Psychology: Learning, Memory, and Cognition and is now a Section Editor for the Open Encyclopedia of Cognitive Science.

Portrait of Carina Dantas
Carina Dantas

Carina Dantas has a degree in Law, is a PhD candidate in Biomedical Sciences with a focus on AI ethics, and is CEO and Chief Investigator of SHINE 2Europe, with more than 25 years of experience in health, social issues, and ICT. She is Co-Director of the SHAFE Foundation and chaired the COST Action NET4Age-Friendly from 2020 to 2024, which brought together more than 775 participants from 50 countries.

She is a committee member of CEN/CENELEC TC 428, contributed to the EU Ethics framework for the ICT profession, and led experts developing reference guidelines on ethics, data privacy, and security for the AAL Programme. She manages international projects and serves as an evaluator, reviewer, and ethics expert for the European Commission, as well as on advisory and ethics boards for international projects.

Call for Papers

Following the success of the first three editions, the HLDM 2026 workshop aims at gathering together a diverse set of researchers addressing the different aspects that characterise effective hybrid decision-making. These range from machine learning approaches that explicitly account for the human-in-the-loop and the downstream goal of the human-machine system, to decision making strategies and HCI principles encouraging a rich and diverse interaction between the human and the machine, to cognitive aspects pinpointing potential pitfalls, misunderstandings and sub-optimal behaviour, legal and regulatory aspects highlighting requirements and constraints that trustworthy and ethical hybrid decision making systems should satisfy. The workshop will feature invited talks, a poster session, presentations of the best contributions and a final discussion.

We invite submissions on a broad range of topics revolving around hybrid human-machine learning and decision-making, including but not limited to:

The goal of the workshop is to foster discussions on the most promising research directions and relevant challenges around hybrid human-machine learning and decision-making. We accept the following types of submissions:

  1. Short papers (6 pages + references) presenting work-in-progress, position papers or open problems with clear and concise formulations of current challenges. Short papers should be anonymized (double-blind review process) and formatted according to the ECMLPKDD 2026 guidelines (see here). Accepted short papers will be included in the Springer Workshop proceedings of ECMLPKDD 2026.

  2. Regular papers (14 pages + references) presenting novel original work not published elsewhere. Regular papers should be anonymized (double-blind review process) and formatted according to the ECMLPKDD 2026 guidelines (see here). Accepted regular papers will be included in the Springer Workshop proceedings of ECMLPKDD 2026. Double-submission of research papers is forbidden.

  3. Non-archival submissions presenting relevant work recently accepted or currently under submission/review at other venues. The original work should be submitted (free format), enriched with a cover page reporting information on why the manuscript is of interest for the workshop. These submissions will not be included in the Springer Workshop proceedings. Non-archival submissions do not require anonymization unless the authors choose to do so because the paper is currently under review at another venue.

We encourage all qualified candidates to submit a paper regardless of age, gender, sexual orientation, religion, country of origin, or ethnicity. All accepted papers will be presented as posters and linked to the workshop page. Submitting a paper to the workshop means that if the paper is accepted at least one author should present it at the workshop. The best contributions will be allocated a 15 min presentation during the workshop to maximize their visibility and impact.

Key Dates:

How to submit:

Go here and create a new submission for the “HLDM: Towards Hybrid Human-Machine Learning and Decision Making” track. Alternatively, create your new submission using this link here.

Workshop Chairs
Andrea Pugnana

Assistant Professor (RTD-A) at the Department of Information Engineering and Computer Science (DISI) of the University of Trento. His research interests span across human-AI collaboration, uncertainty quantification and causal inference. He serves as a reviewer for top ML and AI conferences, e.g., NeurIPS, ICML, ICLR, AISTATS and ECMLPKDD; and has publsihed his works at top ML and AI conferences like NeurIPS, AISTATS and AAAI.

See Andrea's Webpage
Antonia Wüst

PhD student at the Computer Science Department of TU Darmstadt. Her research focuses on abstract visual reasoning through neuro-symbolic methods, specifically program synthesis and concept learning, with a core emphasis on interpretability and human-AI interaction. She serves as a reviewer for top ML and AI conferences and journals, including ICLR, CVPR, UAI, and TMLR; and has published her work at leading venues such as ICML, NeurIPS, UAI, and ECMLPKDD.

See Antonia's Webpage
José M. Alvarez

Senior Research Scientist at Santander AI Lab. His research studies algorithmic tools for decision-making and their societal implications, focusing on applied data science, causal reasoning, and responsible AI. He serves as a reviewer for top ML conferences such as ICML, AISTATS, and ECMLPKDD, and has published at top ML conferences such as AAAI, WSDM, and FAccT. He co-organized the 2nd European Workshop on Algorithmic Fairness (EWAF'23) and oversaw the organization of the third and fourth editions.

See José's Webpage
Andrea Passerini

Full Professor at the Department of Information Engineering and Computer Science (DISI) of the University of Trento and Adjunct Professor at Aalborg University. He is director of the Structured Machine Learning Group and coordinator of the Research Program on Deep and Structured Machine Learning, both at DISI. His research interests include structured machine learning, neuro-symbolic integration, explainable and interactive machine learning, preference elicitation and learning with constraints. He co-authored over 140 refereed papers, and he regularly publishes at top ML and AI conferences and journals like NeurIPS, ICLR, ECMLPKDD, IJCAI, AAAI, MLJ, AIJ and DAMI. He co-organized ECMLPKDD in 2016, AIxIA in 2018, PAIS in 2022 and several workshops and tutorials at top machine learning and AI conferences.

See Andrea's Webpage
Anna Monreale

Associate Professor at the Department of Computer Science of the University of Pisa and an Adjunct Professor at the Faculty of Computer Science of the Dalhousie University. She is vice-coordinator of the National PhD in Artificial Intelligence for the Society of the University of Pisa. Her research interests include Big Data Analytics, Artificial Intelligence, Privacy-by-Design in big data and AI, and Explainable AI. She co-authored over 140 refereed papers published at top ML and AI conferences and journals like ECMLPKDD, SIGKDD, AAAI, DAMI, Artificial Intelligence, and Intelligent Systems. She co-organized several workshops and tutorials at top machine learning and AI conferences.

See Anna's Webpage
Katya Tentori

Full Professor at the University of Trento, affiliated with the Center for Mind/Brain Sciences (CIMeC), the Centre for Medical Sciences (CISMed), and the Department of Psychology and Cognitive Science (DiPSCo). Her research focuses on Bayesian models of confirmation and information search, inductive reasoning, decision biases, probabilistic models of cognition, causality and causal cognition, forecasting models, and applied reasoning and decision-making. She has authored more than 50 peer-reviewed journal articles and publishes in leading scientific venues, including PNAS, Journal of Experimental Psychology: General, Cognition, and Psychonomic Bulletin & Review.

See Katya's Webpage
Contact

For any information please contact hldm-workshop@unitn.it