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Found 4 papers, 1 papers with code
An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning
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.
Explainable Machine Learning with Prior Knowledge: An Overview
This survey presents an overview of integrating prior knowledge into machine learning systems in order to improve explainability.
Supporting verification of news articles with automated search for semantically similar articles
We find that our approach is agnostic to concept drifts, i. e. the machine learning task is independent of the hypotheses in a text.
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
It considers the source of knowledge, its representation, and its integration into the machine learning pipeline.
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