Risk-aware classification via uncertainty quantification

Autonomous and semi-autonomous systems are using deep learning models to improve decision-making. However, deep classifiers can be overly confident in their incorrect predictions, a major issue especially in safety-critical domains. The present study introduces three foundational desiderata for developing real-world risk-aware classification systems. Expanding upon the previously proposed Evidential Deep Learning ( EDL ), we demonstrate the unity between these principles and EDL ’s operational attributes. We then augment EDL empowering autonomous agents to exercise discretion during structured decision-making when uncertainty and risks are inherent. We rigorously examine empirical scenarios to substantiate these theoretical innovations. In contrast to existing risk-aware classifiers, our proposed methodologies consistently exhibit superior performance, underscoring their transformative potential in risk-conscious classification strategies. • Evidential deep learning uses Dirichlet distributions to represent the predictive uncertainty of neural classifiers. • Pignistic probabilities can be used to model rational decision-making under uncertainty. • Risk awareness can be integrated into evidential classifiers using pignistic Dirichlet priors.