Uncertainty-Aware Machine Learning Models for Trustworthy Predictive Decision Support Systems

  • Authors

    • David Wheeler Professor, University of Cambridge, United Kingdom Author
    • Michael Gordon Professor, University of Cambridge, United Kingdom Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2025PI7J6D

    Published 04-04-2025

  • Uncertainty-Aware Machine Learning, Predictive Decision Support Systems, Trustworthy Artificial Intelligence, Bayesian Learning, Explainable Artificial Intelligence (XAI), Decision Intelligence, Uncertainty Quantification, Predictive Analytics, Confidence Estimation, Risk-Aware Decision Making

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    [1]
    D. Wheeler and M. Gordon, “Uncertainty-Aware Machine Learning Models for Trustworthy Predictive Decision Support Systems”, IJMLPA, vol. 8, no. 1, pp. 01–15, Apr. 2025, doi: 10.67228/3142788X/IJMLPA-2025PI7J6D.

  • Abstract

    Predictive Decision Support Systems (PDSS) increasingly rely on machine learning, but conventional models often provide deterministic predictions without measuring uncertainty, limiting their reliability in high-stakes applications. This paper proposes an uncertainty-aware machine learning framework that integrates uncertainty quantification, probabilistic modeling, explainable AI, and adaptive learning to improve prediction reliability, transparency, and decision confidence. The framework models both aleatoric and epistemic uncertainties, producing confidence scores, prediction intervals, and interpretable decision reports instead of single-point predictions. By combining intelligent data preprocessing, uncertainty-aware learning, trustworthy decision intelligence, and continuous model adaptation, the proposed approach enhances robustness, calibration, explainability, and trustworthiness, providing a scalable foundation for reliable predictive decision support in dynamic real-world environments.

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