↓ 跳过正文

公共政策

Can stimulating ownership increase fertility: Evidence from housing interventions in China

·536 字·2 分钟
Polihot 公共政策 比较政治 中国研究 Fertility Housing Policy Homeownership China Policy Evaluation
PNAS 123/31 pp. e2602426123-e2602426123 2026-07-27 Original Can stimulating ownership increase fertility: Evidence from housing interventions in China Zhang Xin, Dongxue Wu, William A. V. Clark Abstract Declining fertility and the emergence of very low TFR's in the Asian economies has increased the focus on how to change the direction of the fertility trend. Several countries in Europe and Asia have explored a variety of stimulus packages to increase the overall TFR. In this research we review those policies and examine one of those interventions which has the potential to stimulate fertility-the role of access to housing. The core of housing approaches to stimulating fertility is to make housing more accessible and to use various forms of credit assistance with the aim of making ownership easier and more attractive to young families. The research asks how effective are these approaches to reversing the decline in fertility? And, are they a solution to very low fertility? The results provide some evidence that the focus on housing stimulus packages may increase fertility although most successfully for socioeconomically advantaged households.

Care Under Strain: Caregiver Mental Health and Early Childhood Development in Rural China

·498 字·1 分钟
Polihot 比较政治 公共政策 中国研究 Caregiver Mental Health Early Childhood Development Rural China Developmental Delays Public Services
JCC — pp. 1-18 2026-07-26 Original Care Under Strain: Caregiver Mental Health and Early Childhood Development in Rural China Lei Wang, Chuhan Tang, Kaiwen Guo, Victoria Tang, Tiffany Zhu, Vivian Zhang, Sophia Li, Amelie Ibel, Anabelle Lin, Scott Rozelle

The backfiring effect of weak AI safety regulation

·828 字·2 分钟
Polihot 公共政策 政治经济学 LLM相关 AI Safety AI Regulation General-Purpose AI Game Theory Regulatory Backfire
PNAS 123/30 pp. e2509768123-e2509768123 2026-07-20 Original The backfiring effect of weak AI safety regulation Benjamin Laufer, Jon Kleinberg, Hoda Heidari Abstract Recent policy proposals aim to improve the safety of general-purpose AI, but there is little understanding of the efficacy of different regulatory approaches. We present a strategic model that explores interactions between safety regulation, general-purpose AI technology creators, and domain specialists-those who adapt the technology for specific applications. Our analysis examines how regulatory measures targeting different parts of the AI development chain affect the outcome of this game. Our model assumes AI technology is characterized by two key attributes: safety and performance. The regulator first sets a minimum safety requirement that applies to one or both players. The general-purpose creator then invests in the technology, establishing its initial safety and performance levels. Next, domain specialists refine the AI for their use cases, updating the safety and performance levels and taking the product to market. Resulting revenue is shared between the specialist and generalist. Our analysis reveals two insights: first, weak safety regulation imposed predominantly on domain specialists can backfire. While it might seem logical to regulate AI use cases, our analysis shows that weak regulations targeting domain specialists alone can reduce safety in a large class of parameterizations. Second, in contrast to the previous finding, we observe that stronger, well-placed regulation can mutually benefit all players. When regulators impose appropriate safety standards on both general-purpose AI creators and domain specialists, the regulation can function as a commitment device, leading to safety and performance gains, surpassing what is achievable under no regulation or regulating only one player.

Race-conscious admissions algorithms and the law

·801 字·2 分钟
Polihot 公共政策 美国政治 Affirmative Action Machine Learning College Admissions Algorithmic Fairness Supreme Court
PNAS 123/30 pp. e2509764123-e2509764123 2026-07-20 Original Race-conscious admissions algorithms and the law Alexandra Chouldechova, Daniel J. Hemel Abstract In recent years, colleges and universities have begun to use machine learning (ML) systems to inform admissions decisions. Meanwhile, in the 2023 case Students for Fair Admissions, Inc. v. President and Fellows of Harvard College , the Supreme Court held that colleges and universities may not make admissions decisions “on the basis of race.” These parallel developments—the rise of ML in admissions and the fall of race-based affirmative action—will force educational institutions, and ultimately courts, to confront the difficult question of what it means for ML systems to differentiate “on the basis of race.” We begin by mapping the Students for Fair Admissions decision onto different uses of race in predictive AI. We distinguish between “first-order” and “second-order” race consciousness at both the training and predictive phases of machine learning, and we argue that each category of race consciousness raises distinct legal and normative issues. We go on to show that the Students for Fair Admissions decision potentially permits—and even endorses—certain forms of race consciousness. Our analysis is grounded in the observation that the process of developing ML-based systems enables policymakers to calibrate decision making algorithms much more precisely and explicitly in response to specific criticisms of race-conscious affirmative action.

Legal infrastructure for transformative AI governance

·427 字·1 分钟
Polihot 公共政策 公共行政 LLM相关 AI Governance Legal Infrastructure Regulatory Markets Frontier Models Autonomous Agents
PNAS 123/30 pp. e2509742123-e2509742123 2026-07-20 Original Legal infrastructure for transformative AI governance Gillian K. Hadfield Abstract Most of our AI governance efforts focus on substance: What rules do we want in place? What limits or checks do we want to impose on AI development and deployment? But a key role for law is not only to establish substantive rules but also to establish legal and regulatory infrastructure to generate and implement rules. The transformative nature of AI calls especially for attention to building legal and regulatory frameworks. In this Perspective, I review three examples: the creation of registration regimes for frontier models; the creation of registration and identification regimes for autonomous agents; and the design of regulatory markets to facilitate a role for private companies to innovate and deliver AI regulatory services.