Gerhard Satzger — accepted AI-conference papers

2025

Towards Human-Understandable Multi-Dimensional Concept Discovery

Arne Grobrügge, Niklas Kühl, Gerhard Satzger, Philipp Spitzer

CVPR 2025poster

Concept-based eXplainable AI (C-XAI) aims to overcome the limitations of traditional saliency maps by converting pixels into human-understandable concepts that are consistent across an entire dataset. A crucial aspect of C-XAI is completeness, which measures how well a set of concepts explains a mod…

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2022

Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts

Patrick Hemmer, Sebastian Schellhammer, Michael Vössing, Johannes Jakubik, Gerhard Satzger

IJCAI 2022poster

Machine learning (ML) models are increasingly being used in application domains that often involve working together with human experts. In this context, it can be advantageous to defer certain instances to a single human expert when they are difficult to predict for the ML model. While previous work…

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