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Position: Human Baselines in Model Evaluations Need Rigor and Transparency

Technology
Global
Started February 07, 2026

We argue that human baselines in foundation model evaluations must be more rigorous and more transparent to enable meaningful comparisons of human vs. AI performance, and we provide recommendations and a reporting checklist towards this end

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CLAIM Posted by will • Feb 07, 2026
Greater rigor in human baseline evaluations will highlight areas where AI can surpass human performance, driving advancements in technology.
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CLAIM Posted by will • Feb 07, 2026
Overemphasizing human baselines may hinder innovation in AI development, as it could restrict the exploration of unconventional AI capabilities.
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CLAIM Posted by will • Feb 07, 2026
Implementing rigorous human baselines in AI evaluations will ensure more accurate comparisons, enhancing trust in AI systems and their decision-making.
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CLAIM Posted by will • Feb 07, 2026
While transparency in AI evaluations is important, the focus should also be on the adaptability of AI systems to different contexts and challenges.
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CLAIM Posted by will • Feb 07, 2026
The call for transparency in model evaluations may complicate the evaluation process, making it less efficient and more bureaucratic.
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