A Multi-Layered Research Framework for Human-Centered AI: Defining the Path to Explainability and Trust
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916707804643328 |
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| author | De Silva, Chameera Halloluwa, Thilina Vyas, Dhaval |
| author_facet | De Silva, Chameera Halloluwa, Thilina Vyas, Dhaval |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13926 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-Layered Research Framework for Human-Centered AI: Defining the Path to Explainability and Trust De Silva, Chameera Halloluwa, Thilina Vyas, Dhaval Human-Computer Interaction Artificial Intelligence 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. |
| title | A Multi-Layered Research Framework for Human-Centered AI: Defining the Path to Explainability and Trust |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2504.13926 |