Misclassification Detection

Misclassification detection focuses on identifying instances where machine learning models make incorrect predictions, a crucial aspect for building trustworthy AI systems. Current research emphasizes developing methods that go beyond simple uncertainty measures, exploring techniques like concept-based explanations, data-driven uncertainty quantification, and the use of outlier samples to improve misclassification identification across various model architectures, including deep learning models for image classification and semantic segmentation. This field is vital for ensuring the reliability and safety of AI applications, particularly in high-stakes domains like medicine, where accurate predictions are paramount for effective decision-making. Improved misclassification detection contributes to more robust and trustworthy AI systems across diverse applications.