Value attributed to text-based archives generated by artificial intelligence

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 (N = 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 they 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.

Automated summary

Openly available NLG algorithms have the potential to generate human-like texts, but they also pose ethical challenges in terms of misinformation. This study investigates how people react to algorithmically generated texts, their ability to distinguish them from original texts, and the value they assign to them. The findings reveal that while participants had difficulty distinguishing between the two types of texts, they were more likely to value and preserve original texts compared to those generated by AI.