高效将神经网络转换为决策树：通过RENTT实现真实解释的路径
Efficiently Transforming Neural Networks into Decision Trees: A Path to Ground Truth Explanations with RENTT
AI总结：
针对神经网络黑箱性质导致的可信度问题，提出RENTT算法，可高效将各类神经网络转换为精确等效决策树，并通过决策树计算不同层面的真实特征重要性，实验证明其在效率、可扩展性及真实解释能力上优于传统方法。
摘要：
尽管神经网络是强大的工具，但其决策的不透明性和黑箱性质阻碍了它们的广泛应用，导致缺乏可信度。为缓解这一问题，可解释人工智能领域的方法试图揭示此类自动化决策的制定过程。但可解释AI方法往往存在忠实性/正确性缺失的问题，即有时提供的解释与神经网络的决策和逻辑不一致。最近，有人提出将神经网络转换为决策树来克服这些问题。遗憾的是，随着神经网络规模的增长，这些方法通常缺乏精确性、可扩展性或可解释性。因此，我们对这些先前的结果进行了推广，特别是考虑了卷积神经网络、循环神经网络、非ReLU激活函数和偏置项。我们的发现伴有严格的证明，并提出了一种新颖的算法RENTT（Runtime Efficient Network to Tree Transformation，运行时高效网络到树转换），旨在以运行时和内存高效的方式计算神经网络的精确等效决策树表示。生成的决策树是多变量的，因此可能过于复杂而难以理解。为缓解这一问题，我们还提供了一种通过等效决策树计算神经网络真实特征重要性的方法——适用于整个模型（全局）、特定输入区域（区域）或单个决策（局部）。所有理论结果都得到了详细数值实验的支持，这些实验强调了两个关键方面：我们算法的计算效率和可扩展性，以及与LIME和SHAP等传统近似方法相比，只有RENTT成功揭示了真实解释。所有代码可在https://github.com/HelenaM23/RENTT获取。
英文摘要：
Although neural networks are a powerful tool, their widespread use is hindered by the opacity of their decisions and their black-box nature, which result in a lack of trustworthiness. To alleviate this problem, methods in the field of explainable Artificial Intelligence try to unveil how such automated decisions are made. But explainable AI methods are often plagued by missing faithfulness/correctness, meaning that they sometimes provide explanations that do not align with the neural network's decision and logic. Recently, transformations to decision trees have been proposed to overcome such problems. Unfortunately, they typically lack exactness, scalability, or interpretability as the size of the neural network grows. Thus, we generalize these previous results, especially by considering convolutional neural networks, recurrent neural networks, non-ReLU activation functions, and bias terms. Our findings are accompanied by rigorous proofs and we present a novel algorithm RENTT (Runtime Efficient Network to Tree Transformation) designed to compute an exact equivalent decision tree representation of neural networks in a manner that is both runtime and memory efficient. The resulting decision trees are multivariate and thus, possibly too complex to understand. To alleviate this problem, we also provide a method to calculate the ground truth feature importance for neural networks via the equivalent decision trees - for entire models (global), specific input regions (regional), or single decisions (local). All theoretical results are supported by detailed numerical experiments that emphasize two key aspects: the computational efficiency and scalability of our algorithm, and that only RENTT succeeds in uncovering ground truth explanations compared to conventional approximation methods like LIME and SHAP. All code is available at https://github.com/HelenaM23/RENTT .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
