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eXplego: An interactive Tool that Helps you Select Appropriate XAI-methods for your Explainability Needs
The growing demand for transparency, interpretability, and explainability of machine learning models and AI systems has fueled the …
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Comparison of Contextual Importance and Utility with LIME and Shapley Values
Different explainable AI (XAI) methods are based on different notions of ‘ground truth’. In order to trust explanations of AI systems, …
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Efficient and simple prediction explanations with groupShapley: A practical perspective
Shapley values has established itself as one of the most appropriate and theoretically sound frameworks for explaining predictions from …
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Explaining predictive models with mixed features using Shapley values and conditional inference trees
It is becoming increasingly important to explain complex, black-box machine learning models. Although there is an expanding literature …
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An approximate Bayesian inversion framework based on local-Gaussian likelihoods
We derive a Bayesian statistical procedure for inversion of geophysical data to rock properties. The procedure is for simplicity …
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Parametric or nonparametric: The FIC approach for stationary time series
We seek to narrow the gap between parametric and nonparametric modelling of stationary time series pro- cesses. The approach is …
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