# Risk-aware Classification via Uncertainty Quantification

arXiv: https://arxiv.org/abs/2412.03391 · HTML: https://mlsanity.com/paper/2412.03391/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 67 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 2 of 2)

- [Deep Deterministic Uncertainty: A Simple Baseline](https://mlsanity.com/paper/2102.11582/) (2021)
- [Deep Deterministic Uncertainty for Semantic Segmentation](https://mlsanity.com/paper/2111.00079/) (2021)

## Built on by (most cited first, 3 of 3)

- [Can LLMs Detect Their Confabulations? Estimating Reliability in Uncertainty-Aware Language Models](https://mlsanity.com/paper/2508.08139/) (2025)
- [SEED-SET: Scalable Evolving Experimental Design for System-level Ethical Testing](https://mlsanity.com/paper/2603.01630/) (2026)
- [Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification](https://mlsanity.com/paper/2604.06032/) (2026)

## Abstract (arXiv)

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.