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Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
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AI-generated faces influence gender stereotypes and racial homogenization.

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Stable Diffusion AI exhibits racial and gender stereotypes, homogenizing depictions of diverse groups. Debiasing methods and inclusive AI images can reduce human biases.

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Social Psychology

Background:

  • Text-to-image generative AI models like Stable Diffusion are widely used.
  • The presence and extent of racial and gender stereotypes in these models are not fully understood.
  • AI-generated imagery may perpetuate societal biases.

Purpose of the Study:

  • To document racial and gender biases in Stable Diffusion.
  • To investigate the degree of racial homogenization in AI image generation.
  • To propose and evaluate debiasing solutions and their impact on human biases.

Main Methods:

  • Analysis of Stable Diffusion outputs across six races, two genders, 32 professions, and eight attributes.
  • Examination of racial similarity in generated images.
  • Development and testing of debiasing techniques allowing user-specified demographic distributions.
  • A preregistered survey experiment assessing the effect of inclusive vs. non-inclusive AI faces on human biases.

Main Results:

  • Significant racial and gender stereotypes were documented in Stable Diffusion.
  • Substantial racial homogenization was observed, with specific groups depicted stereotypically (e.g., Middle Eastern men).
  • Proposed debiasing solutions demonstrated potential for user control over demographic distributions.
  • Exposure to inclusive AI faces reduced human biases, while non-inclusive faces increased them, irrespective of AI labeling.

Conclusions:

  • Text-to-image AI models like Stable Diffusion exhibit significant biases and stereotypes.
  • Racial homogenization is a key issue in current AI image generation.
  • Debiasing strategies and the use of inclusive AI-generated content can mitigate AI-driven and human biases.
  • Addressing biases in AI is crucial for responsible technology development and deployment.