A classifier can follow your rule and still disappoint you because you hadn’t fully worked out what the rule meant. Google Research’s 4 November write-up on Agile Deliberation tackles that problem. You start with a category and its exclusions, then the system finds borderline images for you to judge. Your answers help refine the instructions automatically. For something like ‘healthy food’, an easy salad photo doesn’t reveal much. A smoothie might expose a disagreement about sugar, processing or what should count as a meal. Showing that disagreement gives the person something concrete to decide. The evaluation involved 18 live sessions lasting 90 minutes each, across food classification and misleading reward imagery. Google reports better classification performance and less effort and stress than the manual approach. That’s encouraging, though a small study across two domains can’t settle how it would work for every community or moderation policy. The product idea I like is being shown the cases your instructions don’t cover. I’d find that more useful than another empty box asking me to write a better prompt. https://research.google/blog/agile-deliberation-how-ai-helps-humans-define-the-gray-areas/ submitted by /u/nukebuddy
Originally posted by u/nukebuddy on r/ArtificialInteligence