ForestMonkey: Toolkit for Reasoning with AI-based Defect Detection and Classification Models

TL;DR

ForestMonkey is a Python toolkit that enables reasoning and explainability for AI-based defect detection and classification models by analyzing predictions, features, and providing visual and textual explanations.

Contribution

The paper introduces ForestMonkey, a comprehensive toolkit for reasoning about AI defect detection models, including feature extraction, decision tree reasoning, and performance analysis.

Findings

Effective reasoning visualizations generated for defect detection models

Toolkit demonstrates reasonable time performance across multiple datasets

Provides a user-friendly tutorial for applying reasoning techniques

Abstract

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.