MIT and Google researchers have introduced a post-processing method called WRING to debias vision-language models without disrupting other learned relationships. This approach addresses the “Whac-a-mole dilemma,” where fixing one bias unintentionally creates or amplifies others. The tool is designed to be efficient and non-invasive, allowing developers to improve safety in high-stakes fields like medicine without retraining massive models from scratch.