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Area of Science:

  • Natural Language Generation (NLG)
  • Human-Computer Interaction
  • AI Ethics

Background:

  • Openly available NLG algorithms generate human-like texts, raising ethical concerns like misinformation.
  • Understanding audience reception and evaluation of AI-generated content is crucial.

Purpose of the Study:

  • Investigate audience reactions to algorithmically generated texts.
  • Determine if AI-generated texts are distinguishable from human-generated texts.
  • Assess the value assigned to AI-generated versus human-generated texts.

Main Methods:

  • A preregistered study with 228 participants.
  • Comparison of original text archives with AI-generated text archives.
  • Analysis of participant categorization and value judgments.

Main Results:

  • Participants favored preserving original archives over AI-generated ones.
  • Participants could not accurately distinguish between AI-generated and original archives.
  • Lower value was assigned to texts categorized as AI-generated, influenced by AI attitudes.

Conclusions:

  • Automated text creation impacts reader and writer practices.
  • Audience attitudes toward AI affect the perceived value and use of AI applications.
  • Further research is needed on the societal implications of AI-generated content.