Abstract
The increasing complexity of machine learning models in computer vision, particularly in face verification, requires the development of explainable artificial intelligence (XAI) to enhance interpretability and transparency. This study extends previous work by integrating semantic concepts derived from human cognitive processes into XAI frameworks to bridge the comprehension gap between model outputs and human understanding. We propose a novel approach combining global and local explanations, using semantic features defined by user-selected facial landmarks to generate similarity maps and textual explanations via large language models (LLMs). The methodology was validated through quantitative experiments and user feedback, demonstrating improved interpretability. Results indicate that our semantic-based approach, particularly the most detailed set, offers a more nuanced understanding of model decisions than traditional methods. User studies highlight a preference for our semantic explanations over traditional pixelbased heatmaps, emphasizing the benefits of human-centric interpretability in AI. This work contributes to the ongoing efforts to create XAI frameworks that align AI models behaviour with human cognitive processes, fostering trust and acceptance in critical applications.
Abstract (translated)
在计算机视觉领域，尤其是面部验证中，机器学习模型的复杂性不断增加，需要发展可解释的人工智能（XAI）以提高其可理解性和透明度。本研究扩展了先前的工作，在XAI框架中整合来源于人类认知过程的语义概念，以此弥合模型输出与人理解之间的理解差距。我们提出了一种结合全局和局部解释的新方法，利用用户选择的脸部特征点定义的语义特性生成相似性地图并通过大型语言模型（LLM）提供文本解释。该方法通过定量实验和用户反馈进行了验证，表明其可解释性有所提高。研究结果表明，我们的基于语义的方法，特别是细节最为详尽的一组方法，在理解模型决策方面比传统方法更为细致入微。用户的调查研究表明，他们更喜欢我们提出的语义解释而非传统的像素热图，强调了以人类为中心的可解释性的优势在人工智能中的应用价值。 这项工作为创建与人类认知过程一致的人工智能框架做出了贡献，这些框架旨在建立信任并促进关键应用中对AI模型行为的认可。
URL
https://arxiv.org/abs/2501.05471
