31 citations · 77 across the 6 of their papers we have counts for
6 papers
An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning
Sebastian Müller, Vanessa Toborek, Katharina Beckh +3
The Rashomon Effect describes the following phenomenon: for a given dataset there may exist many models with equally good performance but with different solution strategies. The Ra…
Energy Efficiency Considerations for Popular AI Benchmarks
Raphael Fischer, Matthias Jakobs, Katharina Morik
Advances in artificial intelligence need to become more resource-aware and sustainable. This requires clear assessment and reporting of energy efficiency trade-offs, like sacrifici…
Shapley Values with Uncertain Value Functions
Raoul Heese, Sascha Mücke, Matthias Jakobs +2
We propose a novel definition of Shapley values with uncertain value functions based on first principles using probability theory. Such uncertain value functions can arise in the c…
Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning
Raoul Heese, Thore Gerlach, Sascha Mücke +3
Methods of artificial intelligence (AI) and especially machine learning (ML) have been growing ever more complex, and at the same time have more and more impact on people's lives.…
Explainable Machine Learning with Prior Knowledge: An Overview
Katharina Beckh, Sebastian Müller, Matthias Jakobs +6
This survey presents an overview of integrating prior knowledge into machine learning systems in order to improve explainability. The complexity of machine learning models has elic…
Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework
Katharina Morik, Helena Kotthaus, Raphael Fischer +6
Machine learning applications have become ubiquitous. Their applications range from embedded control in production machines over process optimization in diverse areas (e.g., traffi…