In mission-critical domains such as law enforcement and medical diagnosis, the ability to explain and interpret the outputs of deep learning models is crucial for ensuring user trust and supporting informed decision-making. Despite advancements in explainability, existing methods often fall short in providing explanations that mirror the depth and clarity of those given by human experts. Such expert-level explanations are essential for the dependable application of deep learning models in law enforcement and medical contexts. Additionally, we recognize that most explanations in real-world scenarios are communicated primarily through natural language. Addressing these needs, we propose a novel approach that utilizes characteristic descriptors to explain model decisions by identifying their presence in images, thereby generating expert-like explanations. Our method incorporates a concept bottleneck layer within the model architecture, which calculates the similarity between image and descriptor encodings to deliver inherent and faithful explanations. Through experiments in face recognition and chest X-ray diagnosis, we demonstrate that our approach offers a significant contrast over existing techniques, which are often limited to the use of saliency maps. We believe our approach represents a significant step toward making deep learning systems more accountable, transparent, and trustworthy in the critical domains of face recognition and medical diagnosis.

翻译：在执法和医疗诊断等关键任务领域，深度神经网络输出的可解释性对于确保用户信任和支持知情决策至关重要。尽管可解释性研究已取得进展，现有方法在提供与人类专家解释的深度和清晰度相匹配的说明方面仍存在不足。此类专家级解释对于深度神经网络在执法和医疗场景中的可靠应用至关重要。此外，我们认识到现实场景中的大多数解释主要通过自然语言进行传达。为应对这些需求，我们提出一种创新方法，通过识别图像中特征描述符的存在来解释模型决策，从而生成类专家解释。我们的方法在模型架构中引入概念瓶颈层，通过计算图像与描述符编码的相似度来提供内在且忠实的解释。通过在面部识别和胸部X光诊断任务中的实验，我们证明该方法与现有技术（通常局限于使用显著图）形成显著对比。我们相信，该方法代表着在面部识别和医疗诊断等关键领域使深度学习系统更具问责性、透明度和可信度的重要进展。