  Due to their flexibility and superior performance, machine learning models
frequently complement and outperform traditional statistical survival models.
However, their widespread adoption is hindered by a lack of user-friendly tools
to explain their internal operations and prediction rationales. To tackle this
issue, we introduce the survex R package, which provides a cohesive framework
for explaining any survival model by applying explainable artificial
intelligence techniques. The capabilities of the proposed software encompass
understanding and diagnosing survival models, which can lead to their
improvement. By revealing insights into the decision-making process, such as
variable effects and importances, survex enables the assessment of model
reliability and the detection of biases. Thus, transparency and responsibility
may be promoted in sensitive areas, such as biomedical research and healthcare
applications.
