Shapley Values are concepts established for eXplainable AI. They are used to explain black-box predictive models by quantifying the features' contributions to the model's outcomes. Since computing the exact Shapley Values is known to be computationally intractable on real-world datasets, neural estimators have emerged as alternative, more scalable approaches to get approximated Shapley Values estimates. However, experiments with neural estimators are currently hard to replicate as algorithm implementations, explainer evaluators, and results visualizations are neither standardized nor promptly usable. To bridge this gap, we present BONES, a new benchmark focused on neural estimation of Shapley Value. It provides researchers with a suite of state-of-the-art neural and traditional estimators, a set of commonly used benchmark datasets, ad hoc modules for training black-box models, as well as specific functions to easily compute the most popular evaluation metrics and visualize results. The purpose is to simplify XAI model usage, evaluation, and comparison. In this paper, we showcase BONES results and visualizations for XAI model benchmarking on both tabular and image data. The open-source library is available at the following link: https://github.com/DavideNapolitano/BONES.

翻译：夏普利值是可解释人工智能领域确立的概念，通过量化特征对模型预测结果的贡献来解释黑盒预测模型。由于在实际数据集中计算精确夏普利值被证明是计算不可行的，神经估计器作为替代方案应运而生，为获取近似夏普利值估计提供了更具可扩展性的方法。然而，当前神经估计器的实验难以复现，因为算法实现、解释器评估器和结果可视化既未标准化也无法直接使用。为弥补这一空白，我们提出了BONES——一个专注于夏普利值神经估计的新基准测试。它为研究者提供了一套最先进的神经与传统估计器、一组常用基准数据集、用于训练黑盒模型的专用模块，以及可轻松计算最常用评估指标和可视化结果的特定函数。其目的在于简化XAI模型的使用、评估与比较。本文展示了BONES在表格数据和图像数据上进行XAI模型基准测试的结果与可视化效果。该开源库可通过以下链接获取：https://github.com/DavideNapolitano/BONES。