arXiv:2512.11779v2 Announce Type: replace-cross Abstract: Evaluating conditional coverage remains one of the most persistent challenges in assessing the reliability of predictive systems. Although conformal methods can give guarantees on marginal coverage, no method can guarantee to produce sets with correct conditional coverage, leaving practitioners without a clear way to interpret local deviations. To overcome sample-inefficiency and overfitting issues of existing metrics, we cast conditional coverage estimation as a classification problem. Conditional coverage is violated if and only if so
The increasing deployment of AI systems, particularly in sensitive applications, has heightened the demand for robust methods to assess their reliability and trustworthiness during pre-deployment and post-deployment validation.
Improved diagnostics for conditional coverage in conformal prediction will directly enhance the safety, fairness, and accountability of AI models, which is critical for their societal acceptance and regulatory compliance.
The ability to accurately diagnose conditional coverage issues fundamentally changes how AI system reliability is evaluated and provides a mechanism to identify and correct biases before they manifest in real-world applications.
- · AI ethicists and researchers
- · High-stakes AI application developers (e.g., healthcare, finance)
- · Regulatory bodies
- · Users of AI systems
- · Developers neglecting robust evaluation
- · AI systems with unaddressed biases
- · Risk management firms using outdated assessment methods
More reliable and trustworthy AI systems are developed and deployed in critical applications.
Increased public and regulatory confidence in AI leads to broader adoption and integration across industries.
Standardisation of conditional coverage diagnostics could emerge, fostering a more responsible and transparent AI ecosystem globally.
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Read at arXiv cs.LG