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人工智能生成的基于文本的档案的价值归属。

Value attributed to text-based archives generated by artificial intelligence.

作者信息

Darda Kohinoor, Carre Marion, Cross Emily

机构信息

Penn Center for Neuroaesthetics, University of Pennsylvania, Philadelphia, PA, USA.

School of Psychology, University of Glasgow, Glasgow, UK.

出版信息

R Soc Open Sci. 2023 Feb 8;10(2):220915. doi: 10.1098/rsos.220915. eCollection 2023 Feb.

DOI:10.1098/rsos.220915
PMID:36778947
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9905996/
Abstract

Openly available natural language generation (NLG) algorithms can generate human-like texts across domains. Given their potential, ethical challenges arise such as being used as a tool for misinformation. It is necessary to understand both how these texts are generated from an algorithmic point of view, and how they are evaluated by a general audience. In this study, our aim was to investigate how people react to texts generated algorithmically, whether they are indistinguishable from original/human-generated texts, and the value people assign these texts. Using original text-based archives, and text-based archives generated by artificial intelligence (AI), findings from our preregistered study ( = 228) revealed that people were more likely to preserve original archives compared with AI-generated archives. Although participants were unable to accurately distinguish between AI-generated and original archives, participants assigned lower value to archives categorized as AI-generated compared with those they categorized as original. People's judgements of value were also influenced by their attitudes toward AI. These findings provide a richer understanding of how the emergent practice of automated text creation alters the practices of readers and writers, and have implications for how readers' attitudes toward AI affect the use and value of AI-based applications and creations.

摘要

公开可用的自然语言生成(NLG)算法可以跨领域生成类人文本。鉴于其潜力,出现了一些伦理挑战,比如被用作虚假信息的工具。有必要从算法角度理解这些文本是如何生成的,以及普通受众是如何对其进行评估的。在本研究中,我们的目的是调查人们对算法生成的文本有何反应,它们是否与原创/人工生成的文本难以区分,以及人们赋予这些文本的价值。利用基于原文的存档以及人工智能(AI)生成的基于文本的存档,我们预先注册的研究(n = 228)结果显示,与AI生成的存档相比,人们更倾向于保存原始存档。尽管参与者无法准确区分AI生成的存档和原始存档,但与归类为原始存档的相比,参与者赋予归类为AI生成的存档的价值更低。人们的价值判断也受到他们对AI态度的影响。这些发现更深入地理解了自动文本创作这一新兴实践如何改变读者和作者的行为,并且对读者对AI的态度如何影响基于AI的应用和创作的使用及价值具有启示意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b2f/9905996/6d4db7670828/rsos220915f06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b2f/9905996/60024dabdfc0/rsos220915f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b2f/9905996/7ec9add46447/rsos220915f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b2f/9905996/8ea4b12ed013/rsos220915f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b2f/9905996/0004d3f8b05a/rsos220915f04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b2f/9905996/24026f66b72e/rsos220915f05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b2f/9905996/6d4db7670828/rsos220915f06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b2f/9905996/60024dabdfc0/rsos220915f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b2f/9905996/7ec9add46447/rsos220915f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b2f/9905996/8ea4b12ed013/rsos220915f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b2f/9905996/0004d3f8b05a/rsos220915f04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b2f/9905996/24026f66b72e/rsos220915f05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b2f/9905996/6d4db7670828/rsos220915f06.jpg

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