In healthcare, making the best possible predictions with complex models (eg, neural
networks, ensembles/stacks of different models) can impact patient welfare. In order to make …

The adoption of machine learning in health care hinges on the transparency of the used
algorithms, necessitating the need for explanation methods. However, despite a growing …

A number of techniques have been proposed to explain a machine learning model's
prediction by attributing it to the corresponding input features. Popular among these are …

A barrier to the wider adoption of neural networks is their lack of interpretability. While local
explanation methods exist for one prediction, most global attributions still reduce neural …

Abstract Machine and deep learning survival models demonstrate similar or even improved
time-to-event prediction capabilities compared to classical statistical learning methods yet …

Feature attributions are a common paradigm for model explanations due to their simplicity in
assigning a single numeric score for each input feature to a model. In the actionable …

Feature attributions and counterfactual explanations are popular approaches to explain a
ML model. The former assigns an importance score to each input feature, while the latter …

As machine learning models grow more complex and their applications become more high-
stakes, tools for explaining model predictions have become increasingly important. This has …

Local feature attribution methods are increasingly used to explain complex machine
learning models. However, current methods are limited because they are extremely …

With the availability of large databases and recent improvements in deep learning
methodology, the performance of AI systems is reaching or even exceeding the human level …