↓ 跳过正文

LLM相关

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.

Running With Scissors? Integrating GPT Models Into Public Policy Research

·627 字·2 分钟
Polihot 公共政策 研究方法 LLM相关 Large Language Models GPT Public Policy Text Annotation Advocacy Coalition Framework
PSJ — 2026-06-23 Original Running With Scissors? Integrating GPT Models Into Public Policy Research Giulia Mariani, Allegra H. Fullerton Abstract ABSTRACT The integration of large language models (LLMs) into public policy research presents both exciting opportunities and methodological challenges. This research note explores how OpenAI's GPT can be used to semi‐automate the annotation of legislative testimony within the Advocacy Coalition Framework, focusing on emotion‐belief dyads. Building on Emotion‐Belief Analysis, we demonstrate how GPT can assist in identifying these complex constructs under human supervision. Our contributions are threefold: (1) we provide practical guidance for applying LLMs to publicly available textual data, (2) we propose a semiautomated workflow that strengthens conceptual clarity, transparency, consistency, replicability, and accessibility, and (3) we reflect on the ethical and methodological implications of LLM‐assisted research. As LLMs continue to advance, this research note aims to help scholars balance innovation with rigor and integrate these tools responsibly into policy research, offering lessons that extend to the study of frames, discourses, narratives, and other ideational dimensions of policymaking.

Smiley bots, satisfied citizens? The impact of AI humanization on citizen experience in public services

·553 字·2 分钟
Polihot 公共行政 LLM相关 AI Humanization Public Services Citizen Experience AI Chatbots Survey Experiment
PMR — pp. 1-31 2026-04-02 Original Smiley bots, satisfied citizens? The impact of AI humanization on citizen experience in public services Jinjin Wu, Yifan Chen Abstract This study examines how AI humanization influences citizen experience in public service delivery by integrating the Stereotype Content Model (SCM) and Emotion as Social Information (EASI) theory. Focusing on AI chatbots as a research context, we explore how humanized responses shape perceived warmth and competence and whether effects differ between programmed and non-programmed services through a survey experiment with four vignette-based scenarios. Findings underscore the importance of context-sensitive and carefully calibrated AI design and functioning choices. By linking SCM’s content dimensions with EASI’s affective and inferential mechanisms, the study provides a social-psychological lens to understand citizen–government interactions in AI-driven public administration and provides practical guidance for designing AI-enabled services that remain citizen-friendly while safeguarding core public values.