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CIRCLE: A Framework for Evaluating AI from a Real-World Lens
arXiv – CS AI|Reva Schwartz, Carina Westling, Morgan Briggs, Marzieh Fadaee, Isar Nejadgholi, Matthew Holmes, Fariza Rashid, Maya Carlyle, Afaf Ta\"ik, Kyra Wilson, Peter Douglas, Theodora Skeadas, Gabriella Waters, Rumman Chowdhury, Thiago Lacerda||5 views
🤖AI Summary
Researchers propose CIRCLE, a six-stage framework for evaluating AI systems through real-world deployment outcomes rather than abstract model performance metrics. The framework aims to bridge the gap between theoretical AI capabilities and actual materialized effects by providing systematic evidence for decision-makers outside the AI development stack.
Key Takeaways
- →CIRCLE introduces a lifecycle-based framework to evaluate AI systems based on real-world deployment outcomes rather than model-centric metrics.
- →The framework operationalizes the Validation phase of TEVV by translating stakeholder concerns into measurable signals.
- →Unlike existing approaches, CIRCLE provides prospective rather than retrospective evaluation through coordinated field testing and longitudinal studies.
- →The framework enables governance decisions based on materialized downstream effects rather than theoretical AI capabilities.
- →CIRCLE integrates methods like red teaming and field testing to produce systematic, comparable evidence across different deployment contexts.
#ai-evaluation#framework#real-world-testing#ai-governance#deployment#validation#systematic-evidence#stakeholder-analysis
Read Original →via arXiv – CS AI
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