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

Reva Schwartz, Gabriella Waters

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.

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.
Paper Structure (32 sections, 5 figures, 4 tables)

This paper contains 32 sections, 5 figures, 4 tables.

Table of Contents

  1. FRAME Purpose and Activities
  2. Background
  3. The Next Generation of AI Evaluation
  4. Structural Limitations of the Current Ecosystem for Decision Making
  5. A Decision-Maker's Dilemma
  6. Methodological Pitfalls: AI Evaluation that Mirrors Development
  7. Partial Workarounds
  8. Why this still falls short for decision‑makers
  9. AI Evidence Layers
  10. Layer 1: Abstract knowledge
  11. Layer 2: Contextual knowledge
  12. Layer 3: Systematic knowledge
  13. Analogies from Consumer Technology
  14. How FRAME Works
  15. Testing sandbox
  16. ...and 17 more sections

Figures (5)

  • Figure 1: Modeling user entropy can support concrete AI deployment decisions. (Generative artificial intelligence was used to support the creation of this graphic representing the authors’ own ideas, data, and words on this topic.)
  • Figure 2: The current evaluation ecosystem uses methods that mirror the traditional AI development lifecycle, often neglecting user entropy and suppressing the context needed to make sense of outcomes for decision-making. (Generative artificial intelligence was used to support the creation of this graphic representing the authors’ own ideas, data, and words on this topic.)
  • Figure 3: An example of how three knowledge layers build up evidence across contexts to address the decision‑maker's dilemma. (Generative artificial intelligence was used to support the creation of this graphic representing the authors’ own ideas, data, and words on this topic.)
  • Figure 4: Overview of FRAME’s testing sandbox, metrics hub, and community model architecture. All measures are derived from instrumented sandbox interactions, where panel participants work through realistic scenarios under built‑in safety protections. (Generative artificial intelligence was used to support the creation of this graphic representing the authors’ own ideas, data, and words on this topic.)
  • Figure 5: How different decision lenses position people in AI evaluation.(Generative artificial intelligence was used to support the creation of this graphic representing the authors’ own ideas, data, and words on this topic.)