Title: A Multi-Layered Research Framework for Human-Centered AI: Defining the Path to Explainability and Trust Show Chinese title

Title: 面向人的AI的多层研究框架：定义可解释性和信任的路径

Abstract: The integration of Artificial Intelligence (AI) into high-stakes domains such as healthcare, finance, and autonomous systems is often constrained by concerns over transparency, interpretability, and trust. While Human-Centered AI (HCAI) emphasizes alignment with human values, Explainable AI (XAI) enhances transparency by making AI decisions more understandable. However, the lack of a unified approach limits AI's effectiveness in critical decision-making scenarios. This paper presents a novel three-layered framework that bridges HCAI and XAI to establish a structured explainability paradigm. The framework comprises (1) a foundational AI model with built-in explainability mechanisms, (2) a human-centered explanation layer that tailors explanations based on cognitive load and user expertise, and (3) a dynamic feedback loop that refines explanations through real-time user interaction. The framework is evaluated across healthcare, finance, and software development, demonstrating its potential to enhance decision-making, regulatory compliance, and public trust. Our findings advance Human-Centered Explainable AI (HCXAI), fostering AI systems that are transparent, adaptable, and ethically aligned. Show Chinese abstract

Abstract: 人工智能（AI）在医疗保健、金融和自主系统等高风险领域中的整合，通常受到对透明度、可解释性和信任的担忧所限制。 虽然以人为本的人工智能（HCAI）强调与人类价值观的一致性，可解释的人工智能（XAI）通过使AI决策更易于理解来增强透明度。 然而，缺乏统一的方法限制了AI在关键决策场景中的有效性。 本文提出了一种新颖的三层框架，将HCAI和XAI结合起来，建立一个结构化的可解释性范式。 该框架包括（1）一个具有内置可解释性机制的基础AI模型，（2）一个人本导向的解释层，根据认知负荷和用户专业知识定制解释，以及（3）一个动态反馈循环，通过实时用户交互优化解释。 该框架在医疗保健、金融和软件开发中进行了评估，证明了其在增强决策、监管合规性和公众信任方面的潜力。 我们的研究推进了以人为本的可解释人工智能（HCXAI），促进了透明、灵活且符合伦理的AI系统。