FRAME构建真实场景评估体系，帮决策者看清AI落地效果。
Real-World AI Evaluation: How FRAME Generates Systematic Evidence to Resolve the Decision-Maker's Dilemma
- 结合大规模实测与上下文观察，追踪AI从输出到影响的全过程。
- 在真实工作流中捕捉系统表现，揭示风险与价值分布规律。
- 适合关注AI落地实效的管理者与技术评估团队使用。
组织领导者在缺乏可靠证据的情况下被要求做出高风险的AI部署决策。当前主流评估体系提供可扩展但抽象的指标，反映的是模型开发者的优先级，掩盖了真实使用中用户、流程与环境的多样性，很少揭示实践中风险与价值的分布。更以用户为中心的研究虽具丰富上下文细节，但零散、规模小且与影响模型行为的机制关联松散。论坛「真实世界AI测量与评估」（FRAME）旨在弥合这一缺口，通过大规模AI系统测试与结构化观察其实际使用方式、生成结果及其成因，将AI使用的异质性转化为可度量信号而非规模代价。该论坛建立两大核心资产：测试沙盒（Testing Sandbox），在真实工作流中规模化捕获AI使用情况；度量枢纽（Metrics Hub），将这些使用轨迹转化为可行动的指标。
原文摘要 · Abstract (English)
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.
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