Artificial intelligence (AI) reasoning and explainable AI (XAI) tasks have gained popularity recently, enabling users to explain the predictions or decision processes of AI models. This paper introduces Forest Monkey (FM), a toolkit designed to reason the outputs of any AI-based defect detection and/or classification model with data explainability. Implemented as a Python package, FM takes input in the form of dataset folder paths (including original images, ground truth labels, and predicted labels) and provides a set of charts and a text file to illustrate the reasoning results and suggest possible improvements. The FM toolkit consists of processes such as feature extraction from predictions to reasoning targets, feature extraction from images to defect characteristics, and a decision tree-based AI-Reasoner. Additionally, this paper investigates the time performance of the FM toolkit when applied to four AI models with different datasets. Lastly, a tutorial is provided to guide users in performing reasoning tasks using the FM toolkit.

翻译：人工智能推理与可解释人工智能任务近年来备受关注，能够帮助用户解释AI模型的预测或决策过程。本文介绍了Forest Monkey（FM）工具包，该工具包旨在通过数据可解释性对任何基于AI的缺陷检测和/或分类模型的输出进行推理。FM以Python包形式实现，输入为数据集文件夹路径（包括原始图像、真实标签和预测标签），并输出一组图表和文本文件以展示推理结果并建议可能的改进方向。FM工具包包含从预测到推理目标的特征提取、从图像到缺陷特征的特征提取以及基于决策树的AI推理器等流程。此外，本文还研究了FM工具包在四个不同数据集上的AI模型中的时间性能。最后，提供了使用FM工具包执行推理任务的教程，以指导用户操作。