| _version_ | 1866901751019339776 |
|---|---|
| author | Revista, Zen IA, 10 |
| author_facet | Revista, Zen IA, 10 |
| contents | In an era defined by the pervasive integration of Artificial Intelligence (AI) across various sectors, the opacity of AI decision-making processes presents a significant challenge. This paper introduces a comprehensive framework designed to enhance the interpretability and explainability of AI models. The framework integrates a suite of methods, including rule-based systems, attention mechanisms, and post-hoc explanation techniques, to provide insights into how AI models arrive at their decisions. By synthesizing these approaches, we aim to foster trust, accountability, and transparency in AI systems, ultimately promoting their responsible deployment and utilization. The framework is evaluated across multiple domains, including healthcare, finance, and autonomous systems, demonstrating its versatility and effectiveness in elucidating complex AI decision-making processes. The results underscore the importance of combining multiple interpretability techniques to achieve a holistic understanding of AI behavior and highlight the potential for this framework to guide the development of more transparent and trustworthy AI systems. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17818734 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Unveiling AI Decision-Making: A Comprehensive Framework for Interpretable Methods and Explainable AI Revista, Zen IA, 10 In an era defined by the pervasive integration of Artificial Intelligence (AI) across various sectors, the opacity of AI decision-making processes presents a significant challenge. This paper introduces a comprehensive framework designed to enhance the interpretability and explainability of AI models. The framework integrates a suite of methods, including rule-based systems, attention mechanisms, and post-hoc explanation techniques, to provide insights into how AI models arrive at their decisions. By synthesizing these approaches, we aim to foster trust, accountability, and transparency in AI systems, ultimately promoting their responsible deployment and utilization. The framework is evaluated across multiple domains, including healthcare, finance, and autonomous systems, demonstrating its versatility and effectiveness in elucidating complex AI decision-making processes. The results underscore the importance of combining multiple interpretability techniques to achieve a holistic understanding of AI behavior and highlight the potential for this framework to guide the development of more transparent and trustworthy AI systems. |
| title | Unveiling AI Decision-Making: A Comprehensive Framework for Interpretable Methods and Explainable AI |
| url | https://doi.org/10.5281/zenodo.17818734 |