The publication argues that current efforts to improve the well-being of men and boys of color are too fragmented, failing to account for the structural linkages between systems like education, employment, and criminal justice. It proposes a new, holistic framework that traces how structural conditions flow through multiple domains to measurable outcomes, such as longevity. Key reasoning emphasizes the need to treat 'place' as a fundamental actor and to identify successful communities where these populations are already thriving. Policy implications require shifting away from individual-focused interventions toward developing structural, place-based agendas that address systemic inequities.
In AI evaluation, access is not evidence
English Summary
The article argues that simply granting independent evaluators access to AI companies is insufficient to ensure credible safety assessments; the field must close a critical "measurement gap." Currently, evaluation results are often incomparable, making it difficult for external readers to determine if differences in scores reflect genuine model changes or merely methodological variations. To improve trustworthiness, the report recommends establishing minimum reporting standards, mandatory traceability, and inter-laboratory comparisons of the same systems. Policy strategies must focus on building shared evaluation infrastructure and validating pre-deployment tests against real-world outcomes, thereby transforming AI testing into a reliable, comparable measurement system.
中文摘要
本文提出，僅讓獨立評估機構接觸AI公司，不足以確保可信的安全性評估；該領域必須彌合一個關鍵的「衡量鴻溝」。目前，評估結果往往缺乏可比性，使得外部讀者難以判斷分數差異是源於模型本身的實質變革，還是僅僅是方法論上的差異。為提升可信度，報告建議建立最低報告標準、強制實施可追溯性，並進行針對相同系統的跨實驗室比較。政策策略必須著重於建構共享的評估基礎設施，並將部署前測試與實際世界結果進行驗證，從而將AI測試轉變為一個可靠、可比的衡量系統。
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