arXiv:2605.30615v1 Announce Type: new Abstract: In selective classification, a model predicts the labels of data samples where it is confident, and abstains from predicting labels for samples on which it is not confident. The rejected samples are often labeled by an expert, which is expensive. The budget for the expert is best utilized when the model has low error on non-rejected samples. However, the estimate of a model's confidence might be inconsistent with the model's predictions, which can lead to high error on non-rejected points. Such situations can readily occur in in-context binary cl
The paper addresses an ongoing challenge in AI where models need to balance predictive accuracy with the cost of human intervention, especially as AI systems are deployed in more critical applications.
Improving selective classification directly enhances the reliability and cost-effectiveness of AI systems, making them more practical for real-world scenarios that demand high accuracy or human oversight.
This research provides a method to reduce errors in samples where AI models are confident, meaning human experts can focus their efforts more efficiently on truly uncertain cases.
- · AI developers
- · Industries relying on AI-driven decision-making
- · Companies with high costs for human expert review
More reliable AI systems result in reduced operational costs and increased trust in automated processes.
The improved efficiency of selective classification could accelerate the deployment of AI in sensitive domains like healthcare or autonomous systems.
As AI becomes more reliable, the demand for human experts might shift from routine verification to highly specialized problem-solving on truly ambiguous cases.
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Read at arXiv cs.LG