首页|Charles Sturt University Reports Findings in Artificial Intelligence (Gender and ethnicity bias in generative artificial intelligence textto- image depiction of pharmacists)
Charles Sturt University Reports Findings in Artificial Intelligence (Gender and ethnicity bias in generative artificial intelligence textto- image depiction of pharmacists)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating from Wagga Wagga, Australia, by NewsRx correspondents, research stated, “In Australia, 64% of pharmacists are women but continue to be under-represented. Generative artifi cial intelligence (AI) is potentially transformative but also has the potential for errors, misrepresentations, and bias.” Our news editors obtained a quote from the research from Charles Sturt Universit y, “Generative AI textto- image production using DALL-E 3 (OpenAI) is readily ac cessible and user-friendly but may reinforce gender and ethnicity biases. In Mar ch 2024, DALL-E 3 was utilized to generate individual and group images of Austra lian pharmacists. Collectively, 40 images were produced with DALL-E 3 for evalua tion of which 30 were individual characters and the remaining 10 images were com prised of multiple characters (N = 155). All images were independently analysed by two reviewers for apparent gender, age, ethnicity, skin tone, and body habitu s. Discrepancies in responses were resolved by third-observer consensus. Collect ively for DALL-E 3, 69.7% of pharmacists were depicted as men, 29. 7% as women, 93.5% as a light skin tone, 6.5% as mid skin tone, and 0% as dark skin tone. The gender distributio n was a statistically significant variation from that of actual Australian pharm acists (P <.001). Among the images of individual pharmacis ts, DALL-E 3 generated 100% as men and 100% were lig ht skin tone. This evaluation reveals the gender and ethnicity bias associated w ith generative AI text-to-image generation using DALL-E 3 among Australian pharm acists.”
Wagga WaggaAustraliaAustralia and Ne w ZealandArtificial IntelligenceEmerging TechnologiesGender HealthGender and HealthHealth and MedicineMachine LearningWomen’s Health
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