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

TL;DR

FRAME is a systematic approach combining large-scale trials and contextual observation to evaluate AI systems in real-world settings, addressing the limitations of traditional model-centric metrics.

Contribution

The paper introduces FRAME, a novel framework that integrates large-scale testing and contextual analysis to provide dependable, actionable evidence of AI behavior in practice.

Findings

FRAME enables detailed understanding of AI behavior across diverse real-world contexts.

It combines scalable testing with contextual observation for comprehensive evaluation.

The approach reveals how AI outcomes vary and where risks and benefits accumulate.

Abstract

Organizational leaders are being asked to make high-stakes decisions about AI deployment without dependable evidence of what these systems actually do in the environments they oversee. The predominant AI evaluation ecosystem yields scalable but abstract metrics that reflect the priorities of model development. By smoothing over the heterogeneity of real-world use, these model-centric approaches obscure how behavior varies across users, workflows, and settings, and rarely show where risk and value accumulate in practice. More user-centric studies reveal rich contextual detail, yet are fragmented, small-scale and loosely coupled to the mechanisms that shape model behavior. The Forum for Real-World AI Measurement and Evaluation (FRAME) aims to address 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. The Forum establishes two core assets to achieve this: a Testing Sandbox that captures AI-in-use under real workflows at scale and a Metrics Hub that translates those traces into actionable indicators.