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黑暗未来:人工智能阴谋论信念量表(AICBS)的开发与初步验证

Dark Future: Development and Initial Validation of Artificial Intelligence Conspiracy Beliefs Scale (AICBS).

作者信息

Lin Chung-Ying, Brailovskaia Julia, Üztemur Servet, Gökalp Ali, Değirmenci Nail, Huang Po-Ching, Chen I-Hua, Griffiths Mark D, Pakpour Amir H

机构信息

Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.

Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan.

出版信息

Brain Behav. 2025 Jul;15(7):e70648. doi: 10.1002/brb3.70648.

Abstract

BACKGROUND

In the past few years, the rapid development of artificial intelligence (AI) and its success in many areas of everyday life have attracted global attention. Some discussions have noted that generative AI tools can make decisions on their own with the potential to improve themselves. Subsequently, conspiracy theories have emerged concerning the future implications of AI. In the present study, the Artificial Intelligence Conspiracy Beliefs Scale (AICBS) was developed to assess conspiracy beliefs concerning AI, andits psychometric properties were examined.

METHODS

A cross-sectional survey was conducted with 788 Turkish participants (M= 25.10 years, 56% female). The sample was split to carry out an exploratory factor analysis (EFA; n = 423) and a confirmatory factor analysis (CFA; n = 365), resulting in a 30-item scale comprising five subdimensions.

RESULTS

The five-factor structure explained 62.58% of the total variance. The CFA showed acceptable model fit indices and confirmed the EFA's five-factor structure. Based on the EFA's factor loadings, a short five-item version of the AICBS (AICBS-5) was developed with one item from each subdimension (which explained 45.28% of the variance). The CFA confirmed the unidimensional structure of the AICBS-5. The internal consistency coefficients of the AICBS, its subdimensions, and the AICBS-5 demonstrated very good reliability. Correlation analyses with external criterion measures (AI Anxiety Scale, Generic Conspiracist Beliefs Scale-5, and Anomie) supported the concurrent validity of the AICBS, its subdimensions, and the AICBS-5.

CONCLUSION

The findings demonstrate that both AICBS and AICBS-5 are valid and reliable psychometric instruments to assess AI conspiracy beliefs.

摘要

背景

在过去几年中,人工智能(AI)的快速发展及其在日常生活许多领域的成功引起了全球关注。一些讨论指出,生成式人工智能工具能够自行做出决策,并具有自我改进的潜力。随后,出现了有关人工智能未来影响的阴谋论。在本研究中,开发了人工智能阴谋信念量表(AICBS)来评估有关人工智能的阴谋信念,并检验了其心理测量特性。

方法

对788名土耳其参与者(平均年龄=25.10岁,56%为女性)进行了横断面调查。将样本分为两部分,分别进行探索性因素分析(EFA;n = 423)和验证性因素分析(CFA;n = 365),最终形成了一个包含五个子维度的30个条目的量表。

结果

五因素结构解释了总方差的62.58%。验证性因素分析显示模型拟合指数可接受,并证实了探索性因素分析的五因素结构。基于探索性因素分析的因素负荷,开发了一个简短的五项版AICBS(AICBS-5),每个子维度各选一个条目(解释了45.28%的方差)。验证性因素分析证实了AICBS-5的单维结构。AICBS及其子维度以及AICBS-5的内部一致性系数显示出非常好的信度。与外部标准测量(人工智能焦虑量表、通用阴谋论信念量表-5和失范量表)的相关分析支持了AICBS及其子维度以及AICBS-5的同时效度。

结论

研究结果表明,AICBS和AICBS-5都是评估人工智能阴谋信念的有效且可靠的心理测量工具。

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