Uncertainty-Aware Machine Learning for Reliable Decision-Making in Data-Driven Systems

Abstract

Machine learning models are increasingly deployed in decision-critical environments such as healthcare, finance, and autonomous systems. However, most conventional models generate deterministic predictions without quantifying uncertainty, which can lead to overconfident mispredictions when data are noisy, incomplete, or outside the training distribution. This limitation exposes a critical gap between predictive accuracy and decision reliability in real-world Al systems. To address this challenge, this study proposes an uncertainty-aware machine learning framework that integrates probabilistic modeling techniques into conventional predictive architectures to jointly estimate epistemic and aleatoric uncertainty. The proposed framework enables models to produce predictive distributions rather than single point predictions, allowing systems to quantify confidence and identify high-risk predictions. Experiments were conducted on multiple benchmark datasets representing both classification and regression tasks under varying levels of noise and data incompleteness. The experimental results demonstrate that the proposed framework achieves predictive performance comparable to deterministic baselines while significantly improving reliability and uncertainty calibration. In classification tasks, the model maintained competitive accuracy and F1-scores while providing well-calibrated confidence estimates, whereas in regression experiments the approach reduced prediction risk by identifying high-error cases through increased uncertainty variance. Robustness tests further show that the framework effectively signals degraded prediction reliability when encountering noisy or incomplete inputs. These findings indicate that incorporating uncertainty estimation enhances trustworthiness and robustness without sacrificing predictive performance. The study highlights uncertainty modeling as a critical component for developing reliable and responsible Al systems capable of supporting risk-sensitive decision-making in real-world data-driven environments