The Overlooked Role of Uncertainty in Machine Learning Predictions: A Systematic Review

Precious Chidum; Amadi

doi:10.56201/ijcsmt.vol.12.no2.2026.pg240.257

📄 Download Paper

The Overlooked Role of Uncertainty in Machine Learning Predictions: A Systematic Review

Precious Chidum, Amadi

Abstract

Concerns about the dependability and credibility of prediction outputs have grown as a result of the expanding use of machine learning (ML) systems in high-stakes industries like healthcare, finance, autonomous systems, and public governance. Uncertainty estimate is still somewhat underemphasized, despite its crucial role in risk-sensitive decision-making, whereas model accuracy has historically dominated performance evaluation. By synthesizing recent empirical and theoretical developments published between 2021 and 2024, this systematic review explores the underappreciated significance of uncertainty in machine learning predictions. Peer-reviewed research was found, vetted, and examined in accordance with PRISMA methodological guidelines to investigate the theoretical underpinnings, modeling strategies, assessment metrics, and real-world applications of uncertainty quantification in machine learning systems. In addition to critically assessing popular estimate methods like Bayesian neural networks, deep ensembles, Monte Carlo dropout, evidential learning, and post-hoc calibration techniques, the paper divides uncertainty into epistemic and aleatoric forms. The results show that issues with computational scalability, calibration reliability under distributional shift, inconsistent benchmarking standards, and the restricted incorporation of uncertainty outputs into practical decision frameworks continue to remain problems. The paper also points to shortcomings in uncertainty communication and human–AI interaction, as well as theoretical fragmentation across fields. By offering an integrative conceptual synthesis that connects probabilistic modeling theory with real-world deployment issues, the study adds to the body of knowledge. For developing foundation models, it suggests a systematic research program centered on unified theoretical frameworks, uniform evaluation procedures, domain-sensitive calibration, and scalable uncertainty-aware structures. This paper contributes to the responsible creation of reliable AI systems by redefining uncertainty estimate as essential to machine learning performance rather than as a byproduct.

References

Alsharif, N., Al-Adhaileh, M. H., Alsubari, S. N., & Al-Yaari, M. (2024). ADHD Diagnosis Using Text Features and Predictive Machine Learning and Deep Learning Algorithms. Journal of Disability Research, 3(7). https://doi.org/10.57197/jdr-2024-0082 Goyal, A., Golash, R., & Kumar, A. (2026). Enhancing Aleatoric and Epistemic Uncertainty Prognostication of Financial Data through Volatility-Adaptive Framework. Computational Economics, 4(2). https://doi.org/10.1007/s10614-026-11322-x Hüllermeier, E., & Waegeman, W. (2021). Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Machine Learning, 110(3), 457– 506. https://doi.org/10.1007/s10994-021-05946-3 Jacob, R. A., Wieder, O., Chen, Y., Mazzolari, A., Bergner, A., Schleifer, K.-J., & Kirchmair, J. (2025). Site-of-Metabolism Prediction with Aleatoric and Epistemic Uncertainty Quantification. Journal of Chemical Information and Modeling, 65(16), 8462–8474. https://doi.org/10.1021/acs.jcim.5c00762 Jain, M. (2023). Machine Learning and Deep Learning Approaches for Cybersecurity: A Review. International Journal of Science and Research , 12(10), 1706–1710. https://doi.org/10.21275/sr231023115126 Karimi, H., & Samavi, R. (2023). Quantifying Deep Learning Model Uncertainty in Conformal Prediction. Proceedings of the AAAI Symposium Series, 1(1), 142–148. https://doi.org/10.1609/aaaiss.v1i1.27492 Kong, J., Liu, Y., Yang, G., & Zhou, W. (2024). Conformal prediction for robust deep nonparametric regression. Statistical Papers, 66(1). https://doi.org/10.1007/s00362- 024-01631-4 Milad Rayka, & S Shahab Naghavi. (2025). Uncertainty quantification enables reliable deep learning for protein–ligand binding affinity prediction. Scientific Reports, 15(1), 43156–43156. https://doi.org/10.1038/s41598-025-27167-7 Msaddi, S., & Kumbasar, T. (2026). An Alliance for Uncertainty Quantification: Type-2 Fuzzy Logic Systems With Conformal Prediction. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(2), 1–11. https://doi.org/10.1109/tetci.2025.3647649 Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., & Moher, D. (2021). Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. Journal of Clinical Epidemiology, 134(134), 103–112. https://doi.org/10.1016/j.jclinepi.2021.02.003 Sami Akbulut, & Colak, C. (2024). Next-Generation Healthcare: Artificial Intelligence Applications in Disease Management. Diagnostics, 14(11), 1087–1087. https://doi.org/10.3390/diagnostics14111087 Shobhankumar, D. M. (2023). Uncertainty Quantification in Numerical Methods Using Deep Bayesian Neural Networks. Communications on Applied Nonlinear Analysis, 30(3), 56–66. https://doi.org/10.52783/cana.v30.6095 Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104(1), 333–339. ScienceDirect. https://doi.org/10.1016/j.jbusres.2019.07.039 Tyralis, H., & Papacharalampous, G. (2024). A review of predictive uncertainty estimation with machine learning. Artificial Intelligence Review, 57(4). https://doi.org/10.1007/s10462-023-10698-8 Xiao, Y., & Watson, M. (2019). Guidance on Conducting a Systematic Literature Review. Journal of Planning Education and Research, 39(1), 93–112. Sagepub. https://doi.org/10.1177/0739456X17723971 Yu, L., Liu, C., Yee, J., & Yang, P. (2023). Ensemble deep learning of embeddings for clustering multimodal single-cell omics data. Bioinformatics, 39(6). https://doi.org/10.1093/bioinformatics/btad382

The Overlooked Role of Uncertainty in Machine Learning Predictions: A Systematic Review

Abstract

References

More Articles from INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY