Confidence Calibration

Confidence calibration in machine learning aims to align a model's predicted confidence scores with its actual accuracy, ensuring reliable predictions and trustworthy decision-making. Current research focuses on improving calibration across diverse model types, including large language models (LLMs), vision-language models, and those used in medical image analysis and other applications, employing techniques like temperature scaling, label smoothing, and various regularization methods. Addressing miscalibration—both overconfidence and underconfidence—is crucial for deploying machine learning models safely and effectively in real-world scenarios, particularly in high-stakes domains like healthcare and finance. Improved calibration enhances the interpretability and trustworthiness of AI systems, fostering greater user confidence and facilitating more effective human-AI collaboration.