38 citations · 77 across the 9 of their papers we have counts for
9 papers
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey
Simon Schramm, Christoph Wehner, Ute Schmid
Artificial Intelligence applications gradually move outside the safe walls of research labs and invade our daily lives. This is also true for Machine Learning methods on Knowledge…
Explanatory machine learning for sequential human teaching
Lun Ai, Johannes Langer, Stephen H. Muggleton +1
The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theorie…
Explanation as a process: user-centric construction of multi-level and multi-modal explanations
Bettina Finzel, David E. Tafler, Stephan Scheele +1
In the last years, XAI research has mainly been concerned with developing new technical approaches to explain deep learning models. Just recent research has started to acknowledge…
Expressive Explanations of DNNs by Combining Concept Analysis with ILP
Johannes Rabold, Gesina Schwalbe, Ute Schmid
Explainable AI has emerged to be a key component for black-box machine learning approaches in domains with a high demand for reliability or transparency. Examples are medical assis…
Beneficial and Harmful Explanatory Machine Learning
Lun Ai, Stephen H. Muggleton, Céline Hocquette +2
Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this con…
Verifying Deep Learning-based Decisions for Facial Expression Recognition
Ines Rieger, Rene Kollmann, Bettina Finzel +2
Neural networks with high performance can still be biased towards non-relevant features. However, reliability and robustness is especially important for high-risk fields such as cl…