We conducted 37 semi-structured expert interviews to examine how frontier AI evaluations reduce societal-level AI risks. Evaluations have become central to AI risk governance, despite their shortcomings being widely critiqued. Still, the case for how they reduce risks in practice remains largely implicit. Drawing on expert accounts, we reconstruct this case by identifying the mechanisms through which evaluations are expected to reduce risk, the assumptions underpinning those mechanisms, and the challenges those assumptions face. Our findings suggest that `evaluation-based governance' could contain foundational gaps which undermine risk-reduction strategies that depend on evaluations. Using thematic analysis, we identified three key pathways through which evaluations could reduce risk: by (i) making societal-level risks concrete to decision-makers and the public (raised in 18 out of 37 interviews), (ii) serving as a basis for governance commitments and regulation (18), and (iii) incentivizing a better risk culture within AI companies (16). However, these pathways often depended on shared assumptions that participants themselves doubted. The main challenges included, in order of frequency: (i) diminishing informativeness of evaluation results as model capabilities improve (21), (ii) measurement limitations that may be inherent to the evaluation paradigm (18), (iii) a lack of guarantees that evaluation results will be acted upon by companies or governments (16), and (iv) the degree of control and discretion AI companies retain over evaluation design, disclosure, and response (12). Participants also noted that evaluation-based governance could prove counterproductive, by functioning as a permission structure for developers' continued capability improvement (4). We finish by discussing the implications of our findings for evaluation-based governance, and what would improve its prospects.

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