How to Provide the Reliability of Artificial Intelligence Judges in Statistical Tests?

Large language models (LLM) are increasingly used in data labeling, output evaluation and quality control tasks. However, these applications are observed a serious problem: acting as “real label” directly on artificial intelligence judgments, can lead to misleading results in formal statistical tests.

The research clearly reveals the problems that LLM reviews can do:

• Payment and noise risk: Artificial intelligence judgments can be affected by biases and predictive errors embedded in educational data, which is zealous with the validity of statistical interests.

• Typ-I and Type-II failure control: Mis positive and wrong negative rates in Formal hypothesis tests should be controlled mathematically, but LLM outputs do not automatically ensure this warranty.

• Material-Debalance: The study offers strategies that determine when the cheap AI assessments are used and when human verification is necessary.

• The human escalation: Systems that detect AI weakness for critical decisions and automatically guide human professional are recommended.

This research aims to close the gap between AI and statistical rigor. To increase the reliability of LLM’s incubation, not only technological progress, it has critical importance to maintaining scientific methodology. Especially in areas such as medical research, quality control and compliance tests, AI’s decision-making processes are becoming extremely important.