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Humor as a window into generative AI bias.

Roger Saumure1, Julian De Freitas2, Stefano Puntoni3

  • 1Department of Marketing, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA. saumure@wharton.upenn.edu.

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Summary
This summary is machine-generated.

Generative AI image humor can unintentionally increase discrimination. Making AI-generated images funnier reduces representation of stereotyped race and gender groups but increases representation of older, visually impaired, and high body weight groups.

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Social Science

Background:

  • Generative AI is increasingly used in consumer-facing applications.
  • The impact of AI-driven humor on societal biases is not well understood.
  • Previous research has not systematically examined AI humor's effect on group representation.

Purpose of the Study:

  • To investigate the relationship between humor generation in AI and discrimination.
  • To analyze how AI image modifications for humor affect the representation of various demographic groups.
  • To audit generative AI's output for potential biases introduced through humor.

Main Methods:

  • A preregistered audit of 600 AI-generated images.
  • Utilized 150 diverse prompts for image generation.
  • Analyzed changes in the prevalence of stereotyped groups when images were made "funnier" by ChatGPT.

Main Results:

  • Humor generation in AI led to shifts in the representation of stereotyped groups.
  • Stereotyped groups for race and gender were less likely to be represented in "funnier" images.
  • Stereotyped groups for age, visual impairment, and body weight were more likely to be represented.

Conclusions:

  • AI-driven humor can inadvertently perpetuate or introduce new forms of discrimination.
  • The pursuit of humor in AI systems requires careful consideration of its impact on marginalized groups.
  • Further research is needed to develop AI that is both engaging and equitable.