arXiv:2602.08470v3 Announce Type: replace Abstract: Credal predictors are models that are aware of epistemic uncertainty and produce a convex set of probabilistic predictions. They offer a principled way to quantify predictive epistemic uncertainty (EU) and have been shown to improve model robustness in various settings. However, most state-of-the-art methods mainly define EU as disagreement caused by random training initializations, which mostly reflects sensitivity to optimization randomness rather than uncertainty from deeper sources. To address this, we define EU as disagreement among mode
The continuous push for more reliable and interpretable AI systems, especially in high-stakes applications, is driving innovation in uncertainty quantification, moving beyond optimization randomness.
Improving how AI models quantify epistemic uncertainty directly contributes to more robust and trustworthy AI, which is critical for their adoption in complex decision-making processes.
This research introduces a more principled way to define and measure epistemic uncertainty in AI, shifting focus from mere optimization sensitivity to a deeper understanding of model disagreement between modes.
- · AI researchers and developers
- · Industries requiring high AI reliability (e.g., healthcare, finance, defense)
- · AI model end-users
- · AI models lacking robust uncertainty quantification
- · Current state-of-the-art methods solely relying on initialization randomness
AI systems will become more capable of identifying when they don't know, leading to safer deployments.
Increased trust in AI systems could accelerate their integration into critical infrastructure and autonomous agents.
More reliable AI uncertainty estimates might facilitate new regulatory frameworks for AI safety and accountability.
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Read at arXiv cs.LG