Real-World AI Evaluation: How FRAME Generates Systematic Evidence to Resolve the Decision-Maker's Dilemma

Abstract

The rapid expansion of AI deployments has put organizational leaders in a decision maker's dilemma: they must govern these technologies without systematic evidence of how systems behave in their own environments. Predominant evaluation methods generate scalable, abstract measures of model capabilities but smooth over the heterogeneity of real world use, while user focused testing reveals rich contextual detail yet remains small in scale and loosely coupled to the mechanisms that shape model behavior. The Forum for Real World AI Measurement and Evaluation (FRAME) addresses this gap by combining large scale trials of AI systems with structured observation of how they are used in context, the outcomes they generate, and how those outcomes arise. By tracing the path from an AI system's output through its practical use and downstream effects, FRAME turns the heterogeneity of AI in use into a measurable signal rather than a trade off for achieving scale. FRAME establishes two core assets to accomplish this: a Testing Sandbox that captures AI use under real workflows at scale and a Metrics Hub that translates those traces into actionable indicators.

Reproductions