arXiv:2605.25616v1 Announce Type: new Abstract: Single-pass uncertainty quantification (UQ) methods for classification represent uncertainty by predicting a tractable distribution over the class probability vector. While existing approaches primarily focus on enhancing the expressiveness of this distribution, they often provide limited insight into how predictive uncertainty is structured and aggregated, resulting in weak interpretability. We introduce the courtroom analogy, which conceptualizes uncertainty-aware classification as a structured debate among class-specific advocates. Each advoca
This research provides a fresh conceptual framework for a long-standing challenge in AI regarding uncertainty quantification, indicating a maturing field's self-reflection.
Improved interpretability of AI uncertainty models is critical for deploying AI in high-stakes domains, enhancing trust and auditability for strategic decision-makers.
The proposed 'courtroom analogy' offers a novel lens for designing and evaluating uncertainty-aware classification systems, potentially leading to more transparent and explainable AI outcomes.
- · AI ethicists
- · Developers of explainable AI (XAI)
- · Regulatory bodies
- · High-stakes AI application sectors (e.g., healthcare, finance)
- · Black-box AI models
- · Proprietary uncertainty quantification methods with limited interpretability
This research directly advances the theoretical understanding and practical application of uncertainty quantification in AI.
Better interpretability in UQ could accelerate AI adoption in regulated industries by meeting transparency requirements.
Increased trust in AI's probabilistic outputs might lead to broader societal acceptance and integration of AI in critical decision-making processes.
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Read at arXiv cs.LG