5 papers
Scientific Theory of a Black-Box: A Life Cycle-Scale XAI Framework Based on Constructive Empiricism
Sebastian Müller, Vanessa Toborek, Eike Stadtländer +3
Explainable AI (XAI) offers a growing number of algorithms that aim to answer specific questions about black-box models. What is missing is a principled way to consolidate explanat…
Four Quadrants of Difficulty: A Simple Categorisation and its Limits
Vanessa Toborek, Sebastian Müller, Christian Bauckhage
Curriculum Learning (CL) aims to improve the outcome of model training by estimating the difficulty of samples and scheduling them accordingly. In NLP, difficulty is commonly appro…
Immersive Explainability: Visualizing Robot Navigation Decisions through XAI Semantic Scene Projections in Virtual Reality
Jorge de Heuvel, Sebastian Müller, Marlene Wessels +3
End-to-end robot policies achieve high performance through neural networks trained via reinforcement learning (RL). Yet, their black box nature and abstract reasoning pose challeng…
Beyond Shallow Heuristics: Leveraging Human Intuition for Curriculum Learning
Vanessa Toborek, Sebastian Müller, Tim Selbach +2
Curriculum learning (CL) aims to improve training by presenting data from "easy" to "hard", yet defining and measuring linguistic difficulty remains an open challenge. We investiga…
CFIRE: A General Method for Combining Local Explanations
Sebastian Müller, Vanessa Toborek, Tamás Horváth +1
We propose a novel eXplainable AI algorithm to compute faithful, easy-to-understand, and complete global decision rules from local explanations for tabular data by combining XAI me…