FRAME: Real-World AI Measurement and Evaluation 

Building The Next Generation of AI Evaluation

The Forum for Real‑World AI Measurement and Evaluation (FRAME) is a global initiative anchored at Virginia State University’s Center for Responsible AI building the next generation of AI evaluation by measuring system behavior in real contexts, not just on optimized tests.

The evidence produced from this real-world approach to AI measurement and evaluation helps policymakers, practitioners, and communities deploy these technologies in line with societal goals and operational constraints. ​

Why FRAME

Across sectors, leaders are under pressure to ensure that AI systems deliver value without creating new risks, but the current evaluation ecosystem offers little visibility into how these systems perform in real‑world conditions. Evidence often focuses on abstract model capabilities rather than operational reliability, producing a “decision‑maker’s dilemma,” where stakeholders are left without actionable insight to guide deployment, oversight, or investment.

FRAME refers to the unpredictable and variable ways people interact with AI technology in context as “user entropy,” and treats it as a primary measurement signal—turning it into systematic knowledge that can travel across organizations, domains, and contexts

What FRAME Does

FRAME formalizes real-world AI evaluation methods and translates evaluation outcomes into decision-ready evidence. To do this, FRAME combines large‑scale trials of AI systems with structured observation of how people actually use them, what outcomes they generate, and how those outcomes arise in context. By tracing the path from an AI system’s output through its practical use and downstream consequences, FRAME refines evaluation methodology and generates evidence that helps organizations compare deployments, understand higher‑order effects, and manage AI as an ongoing part of institutional life. ​

To make this work scalable and reusable, FRAME establishes centralized infrastructure that captures “user entropy” at scale and produces comparable indicators across sites:

  • Testing Sandbox – A controlled but realistic environment that uses large‑scale remote participant panels to evaluate AI systems under task‑driven scenarios. Panelists act as reporters of their own experience, documenting how they leverage, repurpose, or abandon tools and where friction, workarounds, or risks appear in everyday use. The sandbox maintains strict human‑subjects protections and relies on carefully designed proxy tasks to measure high‑stakes risks without exposing participants to harm or sensitive content. ​

  • Metrics Hub – A translation layer that converts sandbox traces into indicators of system utility, friction, resilience, access, and impact with real users in real contexts. These indicators sit alongside existing capability, safety, and compliance metrics, adding a deployment‑focused layer that helps leaders interpret what benchmark scores and safety tests mean for actual use over time. ​

Who is Involved

FRAME’s members form a global, interdisciplinary coalition spanning measurement science, machine learning, social science, and the humanities across academia, industry, government, and civil society.

Anchored at Virginia State University’s Center for Responsible AI and managed by Civitaas Insights, the initiative is structured to safeguard independence while providing stable governance and conflict‑of‑interest protections.

With support from sponsoring organizations, FRAME conducts evaluations at scale so sectors can assess AI technologies against their operational realities without exposing proprietary datasets. ​

How Organizations Work with FRAME

Organizations and communities can engage with FRAME to access empirical evidence grounded in settings like their own. Through paid sponsorship tiers, partners can: ​

  • Underwrite sandbox trials tailored to their use cases, such as a benefits chatbot, newsroom tool, or sector‑specific workflow. ​
  • Collaborate on specialized participant panels—such as educators, health professionals, or defined consumer segments—to ensure evaluations reflect the populations and contexts that matter most. ​
  • License access to FRAME’s community models and metrics to compare their own pilots against broader patterns of risk, value, and use without sharing proprietary data or internal systems. ​

FRAME’s methods complement existing capability benchmarks, safety pipelines, and adversarial testing by providing deployment‑focused evidence that clarifies what AI‑in‑use means for workflows, institutions, and communities over time. ​

Governance and Leadership

FRAME is anchored at Virginia State University’s Center for Responsible AI as its institutional sponsor. The institutional sponsor, Director, and Operations Director collectively oversee the Testing Sandbox, Metrics Hub, and member activities, ensuring that all evaluations meet FRAME’s scientific, ethical, and independence standards and remain aligned with its public‑interest mission.

  • Institutional sponsor:
    • Gabriella Waters; Center for Responsible AI, Virginia State University
  • Director:
    • Reva Schwartz; Civitaas Insights LLC
  • Operations Director:
    • Maurice Jones; Center for Responsible AI, Virginia State University

  FRAME Application Link

FRAME (the Forum for Real‑World AI Measurement and Evaluation) is an initiative anchored at Virginia State University’s Center for Responsible AI building up the real-world AI evaluation ecosystem. Leveraging VSU’s shared infrastructure, FRAME conducts sector-based evaluations of commercial AI systems by observing AI‑in‑use and at scale. It then turns those observations into indicators that can support deployment and governance decisions. ​

Most current AI evaluation methods focus on model capabilities in controlled settings, which can miss how systems behave when embedded in real workflows, institutions, and communities. User studies and pilots capture local detail but often remain small scale and hard to compare across sites. Real world AI evaluation fills the gap by treating the variability of AI in use as a core object of measurement, and producing systematic knowledge that connects technical performance to operational outcomes, including AI's higher-order effects.

The decision maker's dilemma describes the situation where leaders must decide whether and how to deploy AI systems without evidence about how those systems behave in the environments they oversee. They may see benchmark scores and lab results, but lack indicators that reflect their own workflows, populations, and risk landscape. FRAME is explicitly designed to help resolve this dilemma by generating decision‑ready evidence about AI in real world contexts.

User entropy is the inherent heterogeneity in how people use AI in context including how they express needs, interpret and adapt outputs, and embed AI into their own goals and constraints. In much of today’s evaluation, this variability is treated as noise to be averaged away; FRAME treats it as a primary signal that shapes whether deployments succeed, stall, or create new risks.

The Testing Sandbox is a controlled but realistic environment where large remote participant panels complete structured scenarios using AI tools under evaluation. Panelists act as reporters of their own experience, documenting how they used, adapted, or abandoned system outputs, while FRAME logs detailed traces of interactions and outcomes. The sandbox uses proxy tasks, human‑subjects protections, and parallel scripted runs to safely study high‑stakes questions and to measure the “reality gap” between idealized model behavior and real-world use.

The Metrics Hub translates sandbox observations into indicators of system usage, utility, friction, access, risk signals, organizational value, and longer‑term societal impact. These indicators are designed to sit alongside existing capability, safety, and compliance metrics, adding a deployment‑focused layer that helps leaders interpret what technical scores mean for their own settings. Over time, FRAME aims to build a shared catalog of these indicators as a common descriptive vocabulary for real‑world AI evaluation.

Traditional benchmarks focus on whether a model can complete specific tasks or follow rules under controlled conditions, often using static datasets and synthetic prompts. FRAME, by contrast, focuses on what happens when people actually use AI tools in realistic scenarios, and on how those interactions accumulate over time into higher‑order effects such as rework, burden shifting, skill change, and new liabilities.

Its methods are designed for deployment stakeholders outside the AI development stack, complementing model‑centric benchmarks by adding ecological, external, and consequential validity for real deployment decisions.

FRAME works with public agencies, civil society organizations, private companies, and community groups that are deploying or governing AI and need evidence about real‑world behavior and impact. Partners can support specific trials, co‑design scenarios and panels that reflect their context, and license metrics and community models to compare their own pilots with broader patterns of use and risk.

No. FRAME is designed to complement existing capability benchmarks, safety pipelines, governance tools, and Testing, Evaluation, Verification, and Validation (TEVV) frameworks. Its methods focus on deployment‑level questions—such as who is benefiting, who is absorbing new risks, and how workflows and outcomes change—and link these insights back to technical and policy requirements.

FRAME does not tap into organizations’ internal data streams or live production environments. Instead, it works with sectors to map constraints and workflows, then recreates them inside the sandbox using proxy scenarios and specialized panels so that sensitive data remain protected. Within the sandbox, panelists’ identities are safeguarded through strict human‑subjects protections, limited data collection, and de‑identification of interaction traces. This approach allows FRAME to capture domain‑specific user entropy and operational patterns while respecting both organizational confidentiality and individual privacy.

Center for Responsible AI Resources

Center for Responsible AI Center for Responsible AI AI Living Labs Network Center for Responsible AI Staff Cognitive and Neurodiversity AI (CoNA) Lab Forum for Real-world AI Measurement and Evaluation (FRAME) FRAME's Mission and Vision Robotics and Digital Twin (RaDiT) Lab

Contact Us

Have questions or want to collaborate? Reach out to the Center for Responsible AI at Virginia State University.

Contact Us