A three-Level Framework for LLM-Enhanced eXplainable AI: From technical explanations to natural language
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915705575702528 |
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| author | Bello, Marilyn Bello, Rafael García, Maria-Matilde Nowé, Ann Sevillano-García, Iván Herrera, Francisco |
| author_facet | Bello, Marilyn Bello, Rafael García, Maria-Matilde Nowé, Ann Sevillano-García, Iván Herrera, Francisco |
| contents | The growing application of artificial intelligence in sensitive domains has intensified the demand for systems that are not only accurate but also explainable and trustworthy. Although explainable AI (XAI) methods have proliferated, many do not consider the diverse audiences that interact with AI systems: from developers and domain experts to end-users and society. This paper addresses how trust in AI is influenced by the design and delivery of explanations and proposes a multilevel framework that aligns explanations with the epistemic, contextual, and ethical expectations of different stakeholders. The framework consists of three layers: algorithmic and domain-based, human-centered, and social explainability, with Large Language Models serving as crucial mediators that transform technical outputs of AI explanations into accessible, contextual narratives across all levels. We show how LLMs enable dynamic, conversational explanations that bridge the gap between complex model behavior and human understanding, facilitating interactive dialogue and enhancing societal transparency. Through comprehensive case studies, we show how this LLM-enhanced approach achieves technical fidelity, user engagement, and societal accountability, reframing XAI as a dynamic, trust-building process that leverages natural language capabilities to democratize AI explainability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05887 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A three-Level Framework for LLM-Enhanced eXplainable AI: From technical explanations to natural language Bello, Marilyn Bello, Rafael García, Maria-Matilde Nowé, Ann Sevillano-García, Iván Herrera, Francisco Artificial Intelligence The growing application of artificial intelligence in sensitive domains has intensified the demand for systems that are not only accurate but also explainable and trustworthy. Although explainable AI (XAI) methods have proliferated, many do not consider the diverse audiences that interact with AI systems: from developers and domain experts to end-users and society. This paper addresses how trust in AI is influenced by the design and delivery of explanations and proposes a multilevel framework that aligns explanations with the epistemic, contextual, and ethical expectations of different stakeholders. The framework consists of three layers: algorithmic and domain-based, human-centered, and social explainability, with Large Language Models serving as crucial mediators that transform technical outputs of AI explanations into accessible, contextual narratives across all levels. We show how LLMs enable dynamic, conversational explanations that bridge the gap between complex model behavior and human understanding, facilitating interactive dialogue and enhancing societal transparency. Through comprehensive case studies, we show how this LLM-enhanced approach achieves technical fidelity, user engagement, and societal accountability, reframing XAI as a dynamic, trust-building process that leverages natural language capabilities to democratize AI explainability. |
| title | A three-Level Framework for LLM-Enhanced eXplainable AI: From technical explanations to natural language |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.05887 |