Value attributed to text-based archives generated by artificial intelligence

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 ( 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.

Humans versus AI: whether and why we prefer human-created compared to AI-created artwork

“…Results of this study indicated that participants tended to prefer the painting that was labelled “human-created” relative to the painting labelled “AI-created.” Notably, this preference for human over AI art is not specific to visual art. In fact, several recent studies have reported similar anti-AI findings in music (Shank et al, 2022 ), creative writing (Raj et al, 2023 ), dance (Darda & Cross, 2023 ), poetry (Köbis & Mossink, 2020 ), and non-art texts (Darda et al, 2023 ). This bias in aesthetic judgement is further intensified when judging more “human” aspects of art, like evoked emotion, suggesting the need for multiple judgement criteria in AI aesthetics research (Raj et al, 2023 ).…”

Impact of artificial intelligence text generators (AITGs) on libraries

“…Furthermore, AI in predictive policing can also include bias against certain racial or ethnic groups by targeting them for more surveillance and policing (Hung and Yen, 2021; Reese, 2022). It is essential to ensure that AITGs are trained on diverse representative data sets free from bias and discrimination to resolve these ethical concerns (Darda et al , 2023). Analogously, the lack of disclosure is unfair to the authors, artificers and original content producers from whom the AITGs have learned because it withholds information about the sources’ origins or needs to acknowledge.…”

“…However, Librarians must carefully examine the output produced by these systems to ensure that it is correct and acceptable for their collections. Remembering that AITGs do not replace human expertise is crucial (Darda et al , 2023). While AITGs have the potential to benefit libraries, if not used responsibly, they can also have adverse effects.…”

“…An artificial intelligence (AI) text generator (AITG) is a software program that uses AI to generate written content, such as writing articles for websites or social media, preparing reports or even authoring creative works and artistic styles like narratives or poems and music, respectively (Wingström et al , 2022; Darda et al , 2023). However, AITGs can perpetuate stereotypes and discrimination, resulting in ethical concerns.…”

Understanding and Perception of Automated Text Generation among the Public: Two Surveys with Representative Samples in Germany

“…In addition, the evaluation of automatically produced content might depend on participants' attitudes toward AI in general. Darda et al [39] found that a positive attitude toward AI leads to higher ratings for both automated and human-generated content. Furthermore, discussions around AI fields like automated driving or AI in healthcare were not based on tools suddenly accessible to everyone.…”