Consistency Metric

Consistency metrics evaluate the reliability and robustness of models, particularly in scenarios with varying inputs or data characteristics. Current research focuses on developing and applying these metrics across diverse domains, including binary classification, large language models (LLMs), and image analysis, often employing information-theoretic measures or permutation-based tests to assess model behavior. These metrics are crucial for identifying and mitigating inconsistencies, improving model trustworthiness, and guiding the development of more reliable and explainable AI systems. The ultimate goal is to enhance the safety and fairness of AI applications by ensuring consistent and predictable model performance.