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Explainable AI: Enhancing Machine Learning Model Interpretability with Generative AI
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 5.39 MB | Adobe PDF |
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Orientador(es)
Resumo(s)
In today’s data-driven world, machine learning (ML) powers critical decisions across sectors
such as healthcare and finance, but the opacity of complex “black-box” models undermines
trust, accountability, and adoption. This thesis tackles the urgent challenge of explainability
by exploring how Large Language Models (LLMs) can transform post-hoc explanations into
clear, actionable insights for diverse stakeholders. Moving beyond traditional techniques like
SHAP and Counterfactuals, which often overwhelm with complexity, this research introduces
a dynamic framework that integrates LLMs as narrative explainers. The methodology
combines robust ML pipelines with post-hoc interpreters, enhanced through prompt
engineering, to generate explanations in both technical and business-friendly formats.
Experiments on real-world datasets, including emergency healthcare and bank fraud
detection, benchmarked leading LLMs such as GPT-4o, Claude 3, LLaMA 3, and DeepSeek.
Results show that GPT-4o consistently delivers the most accurate, fluent, and stakeholderaligned explanations, while local open-weight models offer competitive, privacy-preserving
alternatives. The evaluation, comprising linguistic heuristics, semantic similarity metrics, and
human judgment, demonstrated significant gains in clarity, completeness, and
trustworthiness over conventional explainers. Crucially, counterfactual-based narratives
proved highly intuitive for decision-making, while SHAP-based explanations achieved greater
technical depth. By reframing LLMs as interpretable mediators rather than mere translators,
this study provides empirical evidence that generative AI can close the gap between ML
performance and human understanding. The contributions extend beyond academic insight,
offering practical guidelines for deploying Explainable AI in high-stakes domains where
transparency is not optional but essential for fairness, accountability, and trust.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Explainable AI (XAI) Machine Learning Large Language Models Post Hoc Explainers Interpretability SDG 9 - Industry, innovation and infrastructure SDG 16 - Peace, justice and strong institutions