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The study aims to create a framework for Explainable Artificial Intelligence that promotes transparency and user trust in decision-making.

February 11, 2026Open Access

Interpretable Machine Learning for Transparent Decision-Making: A Conceptual and Applied Framework for Explainable Artificial Intelligence

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The study aims to create a framework for Explainable Artificial Intelligence that promotes transparency and user trust in decision-making.
Developed a three-tiered framework for XAI incorporating model transparency and user-centered design.
Critically analyzed existing XAI methodologies and their limitations.
Applied the framework through case studies in clinical risk prediction and credit scoring.
Implemented explainability mechanisms improved user trust and facilitated regulatory compliance.
Maintained predictive accuracy while enhancing decision quality.
Highlighted the need for context-specific and stakeholder-tailored explanations.

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This conceptual framework enhances decision quality in high-impact sectors, suggesting improved transparency and accountability.

Derar Yahya Baker (2025) studied this question.

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