70 citations · 88 across the 6 of their papers we have counts for
6 papers
Quality Metrics for Transparent Machine Learning With and Without Humans In the Loop Are Not Correlated
Felix Biessmann, Dionysius Refiano
The field explainable artificial intelligence (XAI) has brought about an arsenal of methods to render Machine Learning (ML) predictions more interpretable. But how useful explanati…
A Turing Test for Transparency
Felix Biessmann, Viktor Treu
A central goal of explainable artificial intelligence (XAI) is to improve the trust relationship in human-AI interaction. One assumption underlying research in transparent AI syste…
Sensor Artificial Intelligence and its Application to Space Systems -- A White Paper
Anko Börner, Heinz-Wilhelm Hübers, Odej Kao +15
Information and communication technologies have accompanied our everyday life for years. A steadily increasing number of computers, cameras, mobile devices, etc. generate more and…
A psychophysics approach for quantitative comparison of interpretable computer vision models
Felix Biessmann, Dionysius Irza Refiano
The field of transparent Machine Learning (ML) has contributed many novel methods aiming at better interpretability for computer vision and ML models in general. But how useful the…
Quantifying Interpretability and Trust in Machine Learning Systems
Philipp Schmidt, Felix Biessmann
Decisions by Machine Learning (ML) models have become ubiquitous. Trusting these decisions requires understanding how algorithms take them. Hence interpretability methods for ML ar…
Canonical Trends: Detecting Trend Setters in Web Data
Felix Biessmann, Jens-Michalis Papaioannou, Mikio Braun +1
Much information available on the web is copied, reused or rephrased. The phenomenon that multiple web sources pick up certain information is often called trend. A central problem…