This is the latest in a series of successful ARDUOUS (International Workshop on Annotation of Real World Data for Artificial Intelligence Systems; previously Annotation of useR Data for UbiquitOUs Systems) workshops. The field of Artificial Intelligence (AI) has seen a rapid development in the last years with a huge increase in the consumption of data and in the recognition of its influence on the developed AI systems. To address this shift from knowledge-based to data-driven AI systems development, the ARDUOUS workshop series explore various topics in data annotation for AI applications. Well-annotated data powers developments in many fields of AI such as training models in machine learning, computer vision, and natural language processing, learning representations in knowledge representation and reasoning or planning and search, and plays an important role in validating knowledge-based systems. Furthermore, ensuring the involvement of the relevant stakeholders increases the fairness, ethics and trust in the annotations and ultimately in the resulting AI systems.

Submission Guidelines

We encourage contributions from statisticians, AI researchers, ethicists, and domain experts (e.g., clinicians, legal scholars) addressing the following themes:

• New Taxonomies of Uncertainty: Developing new definitions of uncertainty adapted to the landscape of modern AI. These include definitions that move beyond the standard statistical distinctions to encompass ethical, cultural, and contextual uncertainties central to professional judgment.
• Epistemic vs. Hermeneutic Uncertainty: Distinguishing between “what we don’t know” (epistemic) and “what is open to interpretation” (hermeneutic). How can AI systems signal the latter without falsely quantifying it?
• Methodological Innovations: Novel methods for quantifying uncertainty in generative models trained on near-universal datasets, including metrics for semantic uncertainty, and frameworks for tracking reliability across interactive and multi-agent systems.
• Visualization & Uncertainty Communication: Moving beyond standard confidence intervals through innovations in visual analytics. We seek designs that help users navigate high-dimensional spaces, signal not only statistical uncertainty but also when outputs are technically sound yet open to interpretation, and link uncertainty to downstream decisions.
• Professional Practice & Sense-Making: Participatory frameworks and empirical studies evaluating how uncertainty communication impacts the judgment, accuracy, and agency of human experts in collaborative workflows.

Authors unsure about the suitability of their work are encouraged to submit a preliminary abstract or draft to the editors for feedback. All submissions will undergo the journal’s standard peer review process, handled by the Special Issue Editors.

All papers must be original and not simultaneously submitted to another journal or conference.

Committees

Organising Committee

• Gregory Tourte, University of Oxford, UK
• Kristina Yordanova, University of Greifswald, DE
• Emma Tonkin, University of Bristol, UK

Publication

ARDUOUS 2026 proceedings will be published in the Communications in Computer Science and Information Science Springer series: https://www.springer.com/series/7899

Venue

The 10th International Workshop on Annotation of Real World Data for Artificial Intelligent Systems will be held as part of the German Conference on Artificial Intelligence (KI) 2025 in Bremen, Germany https://ki2026.gi.de/

Contact

All questions about submissions should be emailed to organizers@arduous.eu