ArticleRoyal Society open science2023
Value attributed to text-based archives generated by artificial intelligence.
Article in Royal Society open science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Article
- Review
- Perceptions of artificial intelligence system's aptitude to judge morality and competence amidst the rise of Chatbots.Cognitive research: principles and implications · 2024Article
- Understanding and Perception of Automated Text Generation among the Public: Two Surveys with Representative Samples in Germany.Behavioral sciences (Basel, Switzerland) · 2024Article
- Dancing robots: aesthetic engagement is shaped by stimulus and knowledge cues to human animacy.Frontiers in human neuroscience · 2024Article
- Humans versus AI: whether and why we prefer human-created compared to AI-created artwork.Cognitive research: principles and implications · 2023Article
- Value attributed to text-based archives generated by artificial intelligence.Royal Society open science · 2023Article
- The use of artificial intelligence in a military context: development of the attitudes toward AI in defense (AAID) scale.Frontiers in psychology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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 (
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.