CIRCLE: A Framework for Evaluating AI from a Real-World Lens
TL;DR
CIRCLE operationalizes the Validation phase of TEVV by translating priorities of stakeholders outside the stack into measurable signals, and produces systematic knowledge that can enable governance based on materialized downstream effects rather than theoretical capabilities.
Abstract
This study proposes CIRCLE, a six-stage, lifecycle-based framework to bridge the reality gap between model-centric performance metrics and AI system outcomes in deployment. Current approaches such as MLOps frameworks and AI model benchmarks offer detailed insights into system stability and model capabilities, but they do not provide decision makers outside the AI stack with systematic evidence of how these systems actually behave in real world contexts or affect their organizations over time. CIRCLE operationalizes the Validation phase of TEVV (Test, Evaluation, Verification, and Validation) by translating priorities of stakeholders outside the stack into measurable signals. Unlike participatory design which often remains localized, or algorithmic audits which are often retrospective, CIRCLE provides a structured, prospective protocol for linking context sensitive qualitative insights to scalable quantitative metrics. By integrating methods such as field testing, red teaming, and longitudinal studies into a coordinated pipeline, CIRCLE produces systematic knowledge; evidence that is comparable across sites yet sensitive to local context. This can enable governance based on materialized downstream effects rather than theoretical capabilities.
