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.
Where do chief AI officers go after federal service?
English Summary
Analysis of former Chief AI Officers (CAIOs) reveals that federal service rarely causes radical career shifts; instead, it reinforces or amplifies an individual's existing domain expertise. The key finding is that the most valuable skill gained is not merely AI knowledge, but the practical, boundary-spanning experience of implementing AI within large, complex, and regulated institutions. For policy, this suggests that agencies should prioritize hiring 'battle-tested' executives with deep domain knowledge (e.g., defense or healthcare) over those with only strong AI credentials. Furthermore, the CAIO role itself may become less distinct over time, with AI responsibilities being absorbed into existing organizational functions like the CIO or Chief Data Officer.
中文摘要
分析前首席人工智慧官（CAIO）的經驗顯示，在聯邦政府服務中，很少會造成徹底的職涯轉變；相反地，它往往會鞏固或放大個人現有的領域專業知識。核心發現是，最有價值的技能並非單純的AI知識，而是將AI實施於大型、複雜且受監管的機構所獲得的實務、跨界經驗。對於政策制定而言，這表明各機構應優先聘用具備深厚領域知識（例如國防或醫療保健）且擁有「實戰經驗」的高階主管，而非僅具備強大AI學術背景的人才。此外，CAIO的角色本身也可能隨著時間推移而變得不那麼獨立，其AI職責可能會被吸收進現有的組織職能中，例如資訊長（CIO）或首席資料官（CDO）。
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