Human-centric explainability of AI-based Decision Support Systems (DSS) using visual input modalities is directly related to reliability and practicality of such algorithms. An otherwise accurate and robust DSS might not enjoy trust of experts in critical application areas if it is not able to provide reasonable justification of its predictions. This paper introduces Concept Localization Maps (CLMs), which is a novel approach towards explainable image classifiers employed as DSS. CLMs extend Concept Activation Vectors (CAVs) by locating significant regions corresponding to a learned concept in the latent space of a trained image classifier. They provide qualitative and quantitative assurance of a classifier's ability to learn and focus on similar concepts important for humans during image recognition. To better understand the effectiveness of the proposed method, we generated a new synthetic dataset called Simple Concept DataBase (SCDB) that includes annotations for 10 distinguishable concepts, and made it publicly available. We evaluated our proposed method on SCDB as well as a real-world dataset called CelebA. We achieved localization recall of above 80% for most relevant concepts and average recall above 60% for all concepts using SE-ResNeXt-50 on SCDB. Our results on both datasets show great promise of CLMs for easing acceptance of DSS in practice.

本篇论文提出了一种基于图像分类器的理解性决策支持系统(DSS)  -- 概念定位地图(CLMs)，它能够增强分类器的可靠性并提供合理的预测解释。通过在训练的图像分类器的潜在空间中定位相应的显著区域，CLMs可以提供分类器学习和关注人类在图像识别过程中所注重的相似概念的定性和定量保证。研究人员通过一项新的合成数据集SCDB和一个现实世界数据集CelebA对其进行了评估，并获得了较高的定位召回率，证明了CLMs在实践中缓解DSS受重视度方面具有巨大的潜力。

使用概念定位图解释基于人工智能的决策支持系统