Is Uncertainty Quantification a Viable Alternative to Learned Deferral?

• URL: http://arxiv.org/abs/2508.02319v1
• Date: Mon, 04 Aug 2025 11:37:59 GMT
• Title: Is Uncertainty Quantification a Viable Alternative to Learned Deferral?
• Authors: Anna M. Wundram, Christian F. Baumgartner,
• Abstract summary: One aspect of AI safety is the models' ability to defer decisions to a human expert.<n>During clinical translation, models often face challenges such as data shift.<n>Uncertainty quantification methods may be a promising choice for AI deferral.
• Score: 1.533133219129073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence (AI) holds the potential to dramatically improve patient care. However, it is not infallible, necessitating human-AI-collaboration to ensure safe implementation. One aspect of AI safety is the models' ability to defer decisions to a human expert when they are likely to misclassify autonomously. Recent research has focused on methods that learn to defer by optimising a surrogate loss function that finds the optimal trade-off between predicting a class label or deferring. However, during clinical translation, models often face challenges such as data shift. Uncertainty quantification methods aim to estimate a model's confidence in its predictions. However, they may also be used as a deferral strategy which does not rely on learning from specific training distribution. We hypothesise that models developed to quantify uncertainty are more robust to out-of-distribution (OOD) input than learned deferral models that have been trained in a supervised fashion. To investigate this hypothesis, we constructed an extensive evaluation study on a large ophthalmology dataset, examining both learned deferral models and established uncertainty quantification methods, assessing their performance in- and out-of-distribution. Specifically, we evaluate their ability to accurately classify glaucoma from fundus images while deferring cases with a high likelihood of error. We find that uncertainty quantification methods may be a promising choice for AI deferral.

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