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Journals / Artificial Intelligence Theory and Applications / 2026 / Volume: 6 Issue: 1

Hermai: A Hermeneutic Framework for Context-Aware and Narrative Explainable AI

Şadi Evren Şeker(Istanbul University, Faculty of Computer and Information Technologies, Computer Engineering Department)0000-0002-7323-3695
Zümra Arslanhan(Kocaeli University)
Pages
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Publication Date
2026-05-03
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The framework makes three primary contributions: (1) a context-aware perturbation engine that generates plausible data points by respecting feature correlations, moving beyond the feature independence assumption of simpler methods; (2) a dual-mode explanatory system that generates both local, counterfactual-driven narratives for single predictions and general, rule-based explanations of the model's overall logic using surrogate models; and (3) the synthesis of these elements into human-readable, narrative explanations.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The increasing complexity of machine learning models has created a critical need for explainable artificial intelligence (XAI).
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Key Findings & Data:The framework makes three primary contributions: (1) a context-aware perturbation engine that generates plausible data points by respecting feature correlations, moving beyond the feature independence assumption of simpler methods; (2) a dual-mode explanatory system that generates both local, counterfactual-driven narratives for single predictions and general, rule-based explanations of the model's overall logic using surrogate models; and (3) the synthesis of these elements into human-readable, narrative explanations.
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Conclusion & Impact:We demonstrate Hermai's architecture, its practical application on a classic dataset, and argue that its hermeneutic approach offers a more intuitive, coherent, and trustworthy method for interpreting complex models.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Explainable AI (XAI) · Interpretable Machine Learning · Model-Agnostic

Abstract

The increasing complexity of machine learning models has created a critical need for explainable artificial intelligence (XAI). While prominent post-hoc explanation frameworks like LIME and SHAP have provided foundational tools for model-agnostic interpretation, they often produce atomic feature-attribution scores that lack contextual realism and fail to provide holistic, actionable narratives. This paper introduces Hermai, a novel open-source Python framework designed to bridge this gap. Hermai is founded on the philosophical principles of hermeneutics, positing that true understanding arises from a dialogue between the parts (local predictions) and the whole (global model behavior). The framework makes three primary contributions: (1) a context-aware perturbation engine that generates plausible data points by respecting feature correlations, moving beyond the feature independence assumption of simpler methods; (2) a dual-mode explanatory system that generates both local, counterfactual-driven narratives for single predictions and general, rule-based explanations of the model's overall logic using surrogate models; and (3) the synthesis of these elements into human-readable, narrative explanations. We demonstrate Hermai's architecture, its practical application on a classic dataset, and argue that its hermeneutic approach offers a more intuitive, coherent, and trustworthy method for interpreting complex models.

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Cite This Article

Şeker, Ş. E., Arslanhan, Z. (2026). Hermai: A Hermeneutic Framework for Context-Aware and Narrative Explainable AI. Artificial Intelligence Theory and Applications, Volume: 6 Issue: 1. https://ajindex.com/articles/1609590

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