Publication
Visualizing and Interacting with Model Representation Space for Human-Centric Active Learning
Rida Saghir; Thiago Gouvea; Daniel Sonntag
In: Proceedings of the 35th International Joint Conference on Artificial Intelligence (IJCAI 2026) Demonstrations Track. International Joint Conference on Artificial Intelligence (IJCAI-2026), located at IJCAI-ECAI 2026, August 15-21, Bremen, Germany, IJCAI Organization, 2026.
Abstract
Active learning reduces annotation effort by selecting informative samples, yet most approaches remain model-driven, offering users little control over training or support for understanding model behaviour. Human-centric active learning brings users further into the loop by introducing additional points of interaction, particularly in the sample selection process. However, such systems are typically demonstrated using fixed feature projections or visualizations of shallow classifier outputs. We present a representation-centric active learning tool in which interaction takes place directly within the model’s representation space. By operating in the same space the model uses for decision making, the interface supports the co-evolution of representations and user understanding. We additionally report initial qualitative (think-aloud) and quantitative findings from a pilot study, illustrating that such representation-centric frameworks can achieve comparable performance to standard baselines while fostering improved human–model collaboration. (Video and the code available at \url{https://cst.dfki.de/demo-interacting-model-space}