Abstract :

Machine Learning Models Have Achieved Remarkable Success In Predictive Tasks Across Multiple Domains Including Healthcare, Finance, Cybersecurity, And Recommendation Systems. Despite Their Effectiveness, These Models Often Operate As Opaque Systems That Lack Transparency Regarding The Causes Of Incorrect Predictions. Traditional Explainable Artificial Intelligence (XAI) Approaches Attempt To Interpret Model Behaviour Using External Explanation Tools Or Human-annotated Data, Which Can Be Costly, Difficult To Scale, And Dependent On Domain Expertise. This Study Proposes A Self-Reflective Machine Learning (SRML) Framework Designed To Enable Models To Analyse And Explain Their Own Prediction Failures Without Human Intervention. The Framework Introduces A Failure Reflection Module (FRM) That Monitors Internal Model Signals Such As Prediction Confidence, Feature Importance, And Classification Patterns. By Analysing The Differences Between Correct And Incorrect Predictions, The System Can Autonomously Identify The Potential Causes Of Misclassification And Generate Explanations. Unlike Traditional XAI Techniques, The Proposed Framework Eliminates Dependency On External Explanation Tools And Manually Annotated Explanation Datasets. The Research Develops A Theoretical Architecture, Mathematical Formulation, And Conceptual Evaluation Demonstrating How Self-reflective Learning Can Improve Interpretability And Trustworthiness In Machine Learning Systems. The Results Of This Conceptual Study Suggest That Integrating Failure Reflection Mechanisms Within Machine Learning Models Can Significantly Enhance Transparency While Maintaining Predictive Performance. The Proposed Framework Contributes Toward The Development Of Autonomous, Interpretable, And Trustworthy Artificial Intelligence Systems. Keywords: Explainable AI, Self-Reflective Learning, Model Introspection, Failure Analysis, Trustworthy Machine Learning

Published:

16-3-2026

Issue:

Vol. 26 No. 3 (2026)

Page Nos:

171-179

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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