Overview

Key Dates

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 characterize 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:

• learning to defer

• learning to complement

• selective classification

• cost-sensitive learning

• active learning

• calibration of learning models

• interactive machine learning

• human-in-the-loop machine learning

• trustworthy hybrid decision making

• human-LLM decision making

• cognitive aspects in hybrid decision making

• theoretical models of hybrid decision making

• hybrid decision-making interfaces

• hybrid decision-making strategies

• hybrid decision-making applications

• hybrid decision making in multiparty settings

• human factors and decision making

• regulation of hybrid decision-making systems

• assessment of hybrid decision-making systems

• ethics of hybrid decision-making

• legal aspects of decision support systems

The goal of the workshop is to foster discussion on the most promising research directions and the most relevant challenges revolving around hybrid human-machine learning and decision making. We thus 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:

• Paper Submission Deadline: 7 June 2026

• Paper Author Notification: 7 July 2026

• Workshop date: 7 or 13 September 2026

How to submit:

TBD

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