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Area of Science:

  • Human-computer interaction
  • Artificial intelligence (AI) ethics
  • Cognitive biases

Background:

  • Human-computer interaction (HCI) research highlights user biases against artificial intelligence (AI).
  • Task subjectivity is a proposed source of bias, influencing human judgment of AI outputs.
  • Understanding these biases is crucial as AI's role expands across domains.

Purpose of the Study:

  • To investigate whether users perceive humor generated by AI as less funny than human-generated humor.
  • To examine the impact of perceived AI authorship on the subjective evaluation of creative tasks, specifically humor.
  • To determine if explicit framing of AI authorship alters user bias in rating creative content.

Main Methods:

  • Two experiments were conducted involving human participants rating jokes.
  • Participants evaluated jokes based on perceived source (human vs. AI) and explicit source framing.
  • Joke ratings were compared when participants guessed the source versus when the source was clearly stated.

Main Results:

  • Participants rated jokes attributed to humans as funnier than those attributed to AI when guessing the source.
  • When jokes were explicitly labeled as human or AI-created, no significant difference in funniness ratings was observed.
  • This suggests that perceived authorship, rather than inherent quality, influences humor evaluation.

Conclusions:

  • User bias against AI-generated content, particularly in subjective domains like humor, can be influenced by perceived authorship.
  • Explicitly framing content as AI-created can mitigate negative biases, indicating user attitudes toward AI are malleable.
  • Findings challenge assumptions about fixed biases and highlight the potential for context to shape human-AI interaction dynamics.