GIST Lets AI Filter First, While Humans Review Only the Ambiguous Cases

According to AI TIMES, the GIST research team announced on September 8 that it had developed a data annotation system in which AI handles clear-cut results on its own while people review only the parts that are difficult to judge, cutting annotation time by 68.4% compared with manual work. The team said the approach also reduced time more than methods that combine active learning with AI-assisted annotation, while improving image recognition performance at the same time. The new method uses even unlabeled images for training, with a focus on identifying targets that truly require human attention first. It also does not use the confidence scores produced by AI as-is, but refines them to suit the characteristics of the data and resets the boundary between automated processing and human review. As a result, rather than a workflow in which every output is checked one by one, the effort is being redirected toward a structure that concentrates human labor only on the parts that need verification. The study suggests the potential to change the way training data is built in fields that must continuously accumulate data from new environments, such as autonomous driving and underwater exploration.