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The paper investigates the methodological challenges of using Randomized Controlled Trials (RCTs) and human uplift studies to evaluate frontier AI systems, focusing on their application in high-stakes decision-making contexts. Through interviews with 16 expert practitioners, the study identifies tensions between standard causal inference assumptions and the unique characteristics of rapidly evolving AI. It synthesizes these challenges across the research lifecycle and maps them to practitioner-reported solutions, providing clarity on the limits and appropriate uses of uplift evidence.
Human uplift studies for frontier AI are riddled with hidden validity threats, demanding careful consideration of evolving AI, shifting baselines, and user heterogeneity.
Human uplift studies - or studies that measure AI effects on human performance relative to a status quo, typically using randomized controlled trial (RCT) methodology - are increasingly used to inform deployment, governance, and safety decisions for frontier AI systems. While the methods underlying these studies are well-established, their interaction with the distinctive properties of frontier AI systems remains underexamined, particularly when results are used to inform high-stakes decisions. We present findings from interviews with 16 expert practitioners with experience conducting human uplift studies in domains including biosecurity, cybersecurity, education, and labor. Across interviews, experts described a recurring tension between standard causal inference assumptions and the object of study itself. Rapidly evolving AI systems, shifting baselines, heterogeneous and changing user proficiency, and porous real-world settings strain assumptions underlying internal, external, and construct validity, complicating the interpretation and appropriate use of uplift evidence. We synthesize these challenges across key stages of the human uplift research lifecycle and map them to practitioner-reported solutions, clarifying both the limits and the appropriate uses of evidence from human uplift studies in high-stakes decision-making.