Improving AI models’ ability to explain their predictions

A new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving. Adam Zewe | MIT News Publication Date: March 9, 2026 Press Inquiries Press Contact: Melanie Grados Email: mgrados@mit.edu Phone: 617-253-1682 MIT News Office Media Download ↓ Download Image Caption: A new technique transforms any computer vision model into one that can explain its predictions using a set of concepts a human could understand. Credits: Image: MIT News; iStock *Terms of Use: Images for download on the MIT News office website are made available to non-commercial entities, press and the general public under a Creative Commons Attribution Non-Commercial No Derivatives license. You may not alter the images provided, other than to crop them to size. A credit line must be used when reproducing images; if one is not provided below, credit the images to "MIT." Close Caption: A new technique transforms any computer vision model into one that can explain its predictions using a set of concepts a human could understand. Credits: Image: MIT News; iStock Previous image Next image In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its output.Concept bottleneck modeling is one method that enables artificial intelligence systems to explain their decision-making process. These methods force a deep-learning...