Open-Weight AI Models Require Proportional Evaluation Approaches

Patricia Paskov, Christopher Rodriguez, Sunishchal Dev, Stephen Casper

Expert InsightsPublished May 4, 2026

Open-weight AI models (OWMs) introduce distinct risk factors for which existing evaluation practices, largely designed for closed-weight model deployment, fail to account. The authors propose proportional evaluation (PE) approaches for OWMs, then systematically review current evaluation practices of OWMs released in 2025 through April 2026, finding that only one of the 37 families of models reviewed fulfills PE1–4 and most do not fulfill any.

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Paskov, Patricia, Christopher Rodriguez, Sunishchal Dev, and Stephen Casper, Open-Weight AI Models Require Proportional Evaluation Approaches. Santa Monica, CA: RAND Corporation, 2026. https://www.rand.org/pubs/perspectives/PEA4886-1.html.
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