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The aim is to assess the transparency and interpretability of AI systems in high-stakes applications.

February 2, 2026Open Access

The Rise of Explainable AI: Enhancing Transparency and Trust in Machine Learning Models

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The aim is to assess the transparency and interpretability of AI systems in high-stakes applications.
Reviewed established and emerging interpretability methods including SHAP and LIME.
Evaluated their effectiveness across medical imaging, credit scoring, and compliance scenarios.
Examined the regulatory landscape regarding AI, including GDPR and FDA guidelines.
Discussed ethical challenges surrounding transparency, accountability, and fairness.
Significant progress in post-hoc and intrinsically interpretable model techniques noted.
Challenges include computational complexity and the accuracy-interpretability trade-off.
Diverse stakeholder explanations are needed, with no standardized metrics for evaluation.

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This review evaluates interpretability methods for AI in critical fields, indicating ethical and regulatory implications.

Saliu et al. (2025) studied this question.

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