Otilia Stretcu, Research Scientist, and Leijie Wang, Student Researcher, Google Research
Agile Deliberation addresses the challenges of building AI image classifiers for subjective categories by guiding users through an interactive deliberation workflow, from scoping sub-categories to resolving edge cases, using an LLM-driven optimization loop to automatically build an accurate, aligned classifier.
Machine learning models learn to recognize the world around us from examples labeled by humans. Underlying this process is an assumption that the person doing the labeling has a clear and stable definition of the category in mind before they begin. This assumption holds well when training a model on objective categories, like a stop sign or a golden retriever, where boundaries are clear and universally recognized. In practice, however, real-world applications often require vision systems to navigate the subjective "gray areas" of human experience. For example, consider training a vision classifier to identify "healthy food", curate "inspiring artwork”, or assist in content moderation. In such domains, category boundaries may not be universal or static, since they can shift with cultural context, personal philosophies, or evolving operational policies. Furthermore, people rarely start with a complete specification for subjective concepts, even in their own minds. Instead, they begin with an intuitive idea of a concept and only discover the concept’s true boundaries, exceptions, and nuances as they encounter real-world examples.…
Source: Google Research Blog — Read the full article on the original site ↗