Explainable Artificial Intelligence: A Practical Framework for Interpreting and Trusting Machine Learning Models

Author: Debabrata Pruseth
Publication Date: 2025/11/16
Document Type: Technical Note / Research Article
Language: English

Abstract

Machine learning models are increasingly used in decision-making systems where predictive performance alone is insufficient. In domains such as finance, healthcare, hiring, cybersecurity, public administration, and enterprise risk management, stakeholders require explanations that make model behavior interpretable, auditable, and trustworthy. Explainable Artificial Intelligence (XAI) addresses this need by providing techniques that reveal how models use input features, how predictions are formed, and where model behavior may require additional scrutiny.

This research develops a practical framework for interpreting and trusting machine learning models using a multi-method explainability workflow. The study combines conceptual analysis with an applied implementation using SHAP, LIME, ELI5, DALEX, Partial Dependence Plots, and Individual Conditional Expectation analysis. The implementation uses a tabular income-classification task with 32,561 observations and 12 features. A decision-tree classifier is analyzed through global feature importance, local prediction explanation, decision-path interpretation, model-level performance evaluation, and feature-response analysis. The model achieves an accuracy of 0.84947 and AUC of 0.873046, providing a sufficiently realistic case study for examining explanation behavior without overclaiming predictive validity.  

The proposed framework treats explainability as a lifecycle capability rather than a single post-hoc visualization. The analysis shows that no individual XAI method is sufficient on its own: ELI5 provides readable global summaries, SHAP supports additive feature attribution, LIME explains local decision neighborhoods, DALEX supports model diagnostics, and PDP/ICE reveal feature-response patterns. The study concludes that trustworthy machine learning requires triangulated explanations, governance controls, stakeholder-specific communication, and continuous validation.

Keywords
Explainable AI, XAI, SHAP, LIME, ELI5, DALEX, Partial Dependence Plot, ICE Plot, Interpretable Machine Learning, Decision Tree, AI Governance, Responsible AI, Model Transparency, Trustworthy AI, Feature Attribution

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Explainable Artificial Intelligence: A Practical Framework for Interpreting and Trusting Machine Learning Models

Suggested Citation
Pruseth, D. (2026). Explainable Artificial Intelligence: A Practical Framework for Interpreting and Trusting Machine Learning Models. Debabrata Pruseth AI Blog.

Companion Note
This page provides the abstract and full-text PDF for the research version of the article. A companion blog post explains the same work in a more narrative and implementation-focused style.

Read the companion blog:
https://debabratapruseth.com/a-beginner-friendly-guide-to-explainable-ai-xai/