arXiv:2505.19033v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) is a widely used frequentist framework to quantify uncertainty by constructing prediction sets with user-specified marginal coverage guarantees. In practice, CP is typically applied on top of probabilistic classifiers, which are able to express aleatoric but not epistemic uncertainty. In this paper, we consider the question of how to optimally employ CP on top of a more expressive formalism, namely credal sets, which can express both aleatoric and epistemic uncertainty. More specifically, we propose probabilist
This research addresses a fundamental limitation in current AI uncertainty quantification by building upon recent advancements in Conformal Prediction and the growing need for more robust AI systems.
Improving how AI systems express and quantify both aleatoric and epistemic uncertainty is crucial for deploying reliable and trustworthy AI in high-stakes applications.
This paper proposes a method to integrate credal sets with conformal prediction, offering a more comprehensive approach to uncertainty estimation in AI models.
- · AI safety researchers
- · AI ethics developers
- · High-stakes AI applications (e.g., healthcare, autonomous driving)
- · Academia (ML theory)
- · AI systems with poor uncertainty quantification
- · Developers solely relying on probabilistic classifiers for critical decisions
AI models will be able to provide more nuanced and reliable confidence estimates for their predictions.
This could accelerate the adoption of AI in regulatory-heavy sectors that demand high levels of interpretability and transparency.
Increased trust in AI's uncertainty estimates might shift liability frameworks towards AI systems themselves rather than human operators.
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Read at arXiv cs.LG