4 papers
Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainability
Philipp Vaeth, Alexander M. Fruehwald, Benjamin Paassen +1
Recently, several methods have leveraged deep generative modeling to produce example-based explanations of image classifiers. Despite producing visually stunning results, these met…
Assessing LLM Text Detection in Educational Contexts: Does Human Contribution Affect Detection?
Lukas Gehring, Benjamin PaaÃen
Recent advancements in Large Language Models (LLMs) and their increased accessibility have made it easier than ever for students to automatically generate texts, posing new challen…
Diffusion Classifier Guidance for Non-robust Classifiers
Philipp Vaeth, Dibyanshu Kumar, Benjamin Paassen +1
Classifier guidance is intended to steer a diffusion process such that a given classifier reliably recognizes the generated data point as a certain class. However, most classifier…
Healthy Distrust in AI systems
Benjamin PaaÃen, Suzana Alpsancar, Tobias Matzner +1
Under the slogan of trustworthy AI, much of contemporary AI research is focused on designing AI systems and usage practices that inspire human trust and, thus, enhance adoption of…