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.

介绍 Survex R 包，通过应用可解释的人工智能技术，提供了一种解释任何生存模型的协同框架，能够理解和诊断生存模型，并揭示决策过程中的洞察力，透明度和责任性增强，有助于敏感领域（如生物医学和医疗领域）的可靠模型评估和偏差检测。

survex：用于解释机器学习生存模型的 R 包

survex: an R package for explaining machine learning survival models

A limited set of tools exist for assessing whether the behavior of quantum
machine learning models diverges from conventional models, outside of abstract
or theoretical settings. We present a systematic application of explainable
artificial intelligence techniques to analyze synthetic data generated from a
hybrid quantum-classical neural network adapted from twenty different
real-world data sets, including solar flares, cardiac arrhythmia, and speech
data. Each of these data sets exhibits varying degrees of complexity and class
imbalance. We benchmark the quantum-generated data relative to state-of-the-art
methods for mitigating class imbalance for associated classification tasks. We
leverage this approach to elucidate the qualities of a problem that make it
more or less likely to be amenable to a hybrid quantum-classical generative
model.

使用可解释的人工智能技术对从 20 个真实数据集生成的合成数据进行分析，探究了一种混合量子 - 经典神经网络生成模型的特点和适用性，并与处理类别不平衡的最先进方法进行比较。