Mistrust Score

Mistrust scores quantify the reliability of predictions made by machine learning models, aiming to identify instances where model output should be treated with caution. Current research focuses on developing explainable and actionable scoring frameworks, often employing latent-space embeddings and distance metrics to assess prediction trustworthiness, and incorporating sequential analysis to detect data drift. These advancements are crucial for ensuring the safe and reliable deployment of machine learning models across diverse applications, particularly in high-stakes domains like healthcare and autonomous systems.