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. Traditional training workflows don’t account for this concept evolution, forcing users to provide rigid labels to an AI system even when their own mental model is still shifting.
To bridge this gap, we introduce Agile Deliberation, a framework that treats the creation of subjective AI classifiers as an iterative conversation. Rather than requiring humans to act as static data labelers, Agile Deliberation turns the AI system into a collaborative partner that helps users “deliberate” and sharpen their own definitions. By resolving human ambiguity first, the framework systematically builds more effective, robust, and aligned AI systems. To support the wider research community, we have also open-sourced the underlying code and algorithms on GitHub.
A key differentiator of this work is its focus on the live user experience. Because standard, static AI benchmarks can’t capture subjective concepts, we bypassed simulated testing and evaluated Agile Deliberation across 18 live, 1.5-hour sessions with human participants. This allowed us to directly observe and measure how a person’s intent dynamically evolves when paired with an intelligent system. We found that Agile Deliberation drastically reduced user fatigue and anxiety by replacing manual prompt engineering with direct feedback on borderline images, which resulted in more consistent, higher-performing AI models that outperform standard baselines.