Confidence Scoring

Confidence scoring aims to quantify the reliability of predictions made by machine learning models, particularly in situations where model uncertainty or out-of-distribution data are significant concerns. Current research focuses on improving the calibration and interpretability of confidence scores, exploring techniques like multicalibration for language models and developing alternative metrics like trustworthiness scores for deep neural networks that go beyond simple model-provided confidence. These advancements are crucial for building trust in AI systems across diverse applications, from microscopy image analysis to autonomous systems, by enabling more reliable detection of erroneous predictions and improved decision-making based on model outputs.