A Multi-Layered Research Framework for Human-Centered AI: Defining the Path to Explainability and Trust
This work addresses the problem of limited AI effectiveness in high-stakes domains like healthcare and finance due to transparency concerns, offering a structured framework that is incremental in integrating existing concepts.
The paper tackles the lack of a unified approach for transparency and trust in AI by proposing a three-layered framework that bridges Human-Centered AI and Explainable AI, demonstrating its potential to enhance decision-making, regulatory compliance, and public trust across healthcare, finance, and software development domains.
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.