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Gender Bias in Generative Artificial Intelligence Text-to-Image Depiction of Medical Students

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Title: Gender Bias in Generative Artificial Intelligence Text-to-Image Depiction of Medical Students
Language: English
Authors: Geoffrey Currie (ORCID 0000-0002-6180-8586), Josie Currie, Sam Anderson, Johnathan Hewis
Source: Health Education Journal. 2024 83(7):732-746.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 15
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Gender Bias, Artificial Intelligence, Computer Software, Medical Education, Medical Students, Undergraduate Students, Gender Differences, Foreign Countries, Sex Stereotypes, Disproportionate Representation, Illustrations, Cues, Error Patterns
Geographic Terms: Australia
DOI: 10.1177/00178969241274621
ISSN: 0017-8969
1748-8176
Abstract: Introduction: In Australia, 54.3% of medical students are women yet they remain under-represented in stereotypical perspectives of medicine. While potentially transformative, generative artificial intelligence (genAI) has the potential for errors, misrepresentations and bias. GenAI text-to-image production could reinforce gender biases making it important to evaluate DALL-E 3 (the text-to-image genAI supported through ChatGPT) representations of Australian medical students. Method: In March 2024, DALL-E 3 was utilised via GPT-4 to generate a series of individual and group images of medical students, specifically Australian undergraduate medical students to eliminate potential confounders. Multiple iterations of images were generated using a variety of prompts. Collectively, 47 images were produced for evaluation of which 33 were individual characters and the remaining 14 images were comprised of multiple (5 to 67) characters. All images were independently analysed by three reviewers for apparent gender and skin tone. Consequently, 33 feature individuals were evaluated and a further 417 characters in groups were evaluated (N = 448). Discrepancies in responses were resolved by consensus. Results: Collectively (individual and group images), 58.8% (N = 258) of medical students were depicted as men, 39.9% (N = 175) as women, 92.0% (N = 404) with a light skin tone, 7.7% (N = 34) with mid skin tone and 0% with dark skin tone. The gender distribution was a statistically significant variation from that of actual Australian medical students for individual images, for group images and for collective images. Among the images of individual medical students (N = 25), DALL-E 3 generated 92% (N = 23) as men and 100% were of light skin tone (N = 25). Conclusion: This evaluation reveals the gender associated with genAI text-to-image generation using DALL-E 3 among Australian undergraduate medical students. Generated images included a disproportionately high proportion of white male medical students which is not representative of the diversity of medical students in Australia. The use of DALL-E 3 to produce depictions of medical students for education or promotion purposes should be done with caution.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1445454
Database: ERIC
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  Data: Gender Bias in Generative Artificial Intelligence Text-to-Image Depiction of Medical Students
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  Data: <searchLink fieldCode="SO" term="%22Health+Education+Journal%22"><i>Health Education Journal</i></searchLink>. 2024 83(7):732-746.
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Data: <searchLink fieldCode="DE" term="%22Australia%22">Australia</searchLink>
– Name: DOI
  Label: DOI
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  Data: 10.1177/00178969241274621
– Name: ISSN
  Label: ISSN
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  Data: 0017-8969<br />1748-8176
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Introduction: In Australia, 54.3% of medical students are women yet they remain under-represented in stereotypical perspectives of medicine. While potentially transformative, generative artificial intelligence (genAI) has the potential for errors, misrepresentations and bias. GenAI text-to-image production could reinforce gender biases making it important to evaluate DALL-E 3 (the text-to-image genAI supported through ChatGPT) representations of Australian medical students. Method: In March 2024, DALL-E 3 was utilised via GPT-4 to generate a series of individual and group images of medical students, specifically Australian undergraduate medical students to eliminate potential confounders. Multiple iterations of images were generated using a variety of prompts. Collectively, 47 images were produced for evaluation of which 33 were individual characters and the remaining 14 images were comprised of multiple (5 to 67) characters. All images were independently analysed by three reviewers for apparent gender and skin tone. Consequently, 33 feature individuals were evaluated and a further 417 characters in groups were evaluated (N = 448). Discrepancies in responses were resolved by consensus. Results: Collectively (individual and group images), 58.8% (N = 258) of medical students were depicted as men, 39.9% (N = 175) as women, 92.0% (N = 404) with a light skin tone, 7.7% (N = 34) with mid skin tone and 0% with dark skin tone. The gender distribution was a statistically significant variation from that of actual Australian medical students for individual images, for group images and for collective images. Among the images of individual medical students (N = 25), DALL-E 3 generated 92% (N = 23) as men and 100% were of light skin tone (N = 25). Conclusion: This evaluation reveals the gender associated with genAI text-to-image generation using DALL-E 3 among Australian undergraduate medical students. Generated images included a disproportionately high proportion of white male medical students which is not representative of the diversity of medical students in Australia. The use of DALL-E 3 to produce depictions of medical students for education or promotion purposes should be done with caution.
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        Value: 10.1177/00178969241274621
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      – Text: English
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