Gender bias in text-to-image generative artificial intelligence depiction of Australian paramedics and first responders.

Journal: Australasian emergency care
Published Date:

Abstract

INTRODUCTION: In Australia, almost 50 % of paramedics are female yet they remain under-represented in stereotypical depictions of the profession. The potentially transformative value of generative artificial intelligence (AI) may be limited by stereotypical errors, misrepresentations and bias. Increasing use of text-to-image generative AI, like DALL-E 3, could reinforce gender and ethnicity biases and, therefore, is important to objectively evaluate.

Authors

  • Geoffrey Currie
    School of Dentistry & Health Sciences, Charles Sturt University, Wagga Wagga, Australia. Electronic address: gcurrie@csu.edu.au.
  • Johnathan Hewis
    School of Dentistry & Medical Sciences, Charles Sturt University, Port Macquarie, Australia.
  • Phillip Ebbs
    Charles Sturt University, Port Macquarie, Australia.