Suppr超能文献

公众对人工智能科学及科学家的看法相对更负面,但与对普通科学和气候科学的看法相比,政治化程度更低。

Public perceptions of AI science and scientists relatively more negative but less politicized than general and climate science.

作者信息

Walter Dror, Ophir Yotam, Jamieson Patrick E, Jamieson Kathleen Hall

机构信息

Department of Communication, Georgia State University, Atlanta, GA 30303, USA.

Department of Communication, University at Buffalo, Buffalo, NY 14260, USA.

出版信息

PNAS Nexus. 2025 Jun 17;4(6):pgaf163. doi: 10.1093/pnasnexus/pgaf163. eCollection 2025 Jun.

Abstract

Using a weighted 2023-2025 national probability panel of US adults, we compared the perceived Credibility, Prudence, Unbiasedness, Self-Correction, and Benefit (i.e. Factors Assessing Science's Self-Presentation [FASS]) of AI scientists with those of scientists in general and climate scientists in particular. Our analysis reveals that respondents' composite perceptions of AI scientists are the most negative of the three, a difference driven by a facet of the Prudence factor, specifically the perception that AI science is causing unintended consequences; political ideology and patterns of media exposure are substantially more predictive of perceptions of climate science and science in general than of AI; and FASS and respondent ideology predict more variance in support for federal funding of the other two than of AI.

摘要

我们使用了一个基于2023 - 2025年美国成年人全国概率样本的加权面板,比较了人们对人工智能科学家的可信度、审慎性、无偏见性、自我修正能力和益处(即评估科学自我呈现的因素 [FASS])的认知与对一般科学家尤其是气候科学家的认知。我们的分析表明,受访者对人工智能科学家的综合认知在这三者中最为负面,这一差异是由审慎因素的一个方面驱动的,具体而言是认为人工智能科学正在造成意外后果;政治意识形态和媒体接触模式对气候科学和一般科学认知的预测能力,要比对人工智能认知的预测能力强得多;而且FASS和受访者意识形态对支持其他两类科学家获得联邦资金的差异预测能力,比对支持人工智能科学家获得联邦资金的差异预测能力更强。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec43/12199247/857832a362ee/pgaf163f1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验