Uncertainty quantification in deep neural networks: Techniques and applications in autonomous decision-making systems

Abstract

Uncertainty quantification (UQ) in deep neural networks (DNNs) is an essential area of research, particularly for enhancing the reliability and safety of autonomous decision-making systems deployed in high-stakes environments such as autonomous vehicles, healthcare, and robotics. This article provides a comprehensive overview of the key techniques for UQ in DNNs, including Bayesian Neural Networks, Monte Carlo Dropout, ensemble methods, and Gaussian Processes, highlighting their respective strengths and limitations. The applications of UQ in critical domains are examined, demonstrating how these techniques contribute to safer and more informed decision-making processes. The article also discusses the challenges faced in implementing UQ, such as computational complexity, scalability, and interpretability, as well as the limitations of current methods. Future directions for research are explored, emphasizing the need for more efficient, interpretable, and scalable UQ techniques, as well as the importance of integrating UQ into the AI development lifecycle and addressing ethical considerations. The article concludes by underscoring the critical role of UQ in the advancement of robust and trustworthy AI systems capable of operating effectively in uncertain real-world environments.