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

• URL: http://arxiv.org/abs/2504.13926v2
• Date: Sat, 26 Apr 2025 01:53:27 GMT
• Title: A Multi-Layered Research Framework for Human-Centered AI: Defining the Path to Explainability and Trust
• Authors: Chameera De Silva, Thilina Halloluwa, Dhaval Vyas,
• Abstract summary: Human-Centered AI (HCAI) emphasizes alignment with human values, while Explainable AI (XAI) enhances transparency by making AI decisions more understandable.<n>This paper presents a novel three-layered framework that bridges HCAI and XAI to establish a structured explainability paradigm.<n>Our findings advance Human-Centered Explainable AI (HCXAI), fostering AI systems that are transparent, adaptable, and ethically aligned.
• Score: 2.4578723416255754
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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