论文标题
用于工业质量控制的互动解释性AI系统
An Interactive Explanatory AI System for Industrial Quality Control
论文作者
论文摘要
基于机器学习的图像分类算法，例如深神经网络方法，将越来越多地用于诸如行业质量控制之类的关键环境，在这种情况下，决策的透明度和可理解性至关重要。因此，我们旨在将缺陷检测任务扩展到交互式的人类在循环方法上，该方法使我们能够整合丰富的背景知识和复杂关系的推断，而超越了传统的纯粹数据驱动的方法。我们为在工业质量控制环境中进行分类的交互式支持系统提出了一种方法，该方法结合了（可解释的）知识驱动和数据驱动的机器学习方法的优势，特别是归纳逻辑编程和卷积神经网络以及人类的专业知识和控制。最终的系统可以帮助域专家做出决策，为结果提供透明的解释，并整合用户的反馈；因此，减少人类的工作量，同时既尊重他们的专业知识，又不删除其代理或问责制。
Machine learning based image classification algorithms, such as deep neural network approaches, will be increasingly employed in critical settings such as quality control in industry, where transparency and comprehensibility of decisions are crucial. Therefore, we aim to extend the defect detection task towards an interactive human-in-the-loop approach that allows us to integrate rich background knowledge and the inference of complex relationships going beyond traditional purely data-driven approaches. We propose an approach for an interactive support system for classifications in an industrial quality control setting that combines the advantages of both (explainable) knowledge-driven and data-driven machine learning methods, in particular inductive logic programming and convolutional neural networks, with human expertise and control. The resulting system can assist domain experts with decisions, provide transparent explanations for results, and integrate feedback from users; thus reducing workload for humans while both respecting their expertise and without removing their agency or accountability.
