Gender and Ethnicity Representation of University Academics by Generative Artificial Intelligence Using DALL-E 3
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| Title: | Gender and Ethnicity Representation of University Academics by Generative Artificial Intelligence Using DALL-E 3 |
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| Language: | English |
| Authors: | G. Currie (ORCID |
| Source: | Journal of Further and Higher Education. 2025 49(8):1064-1078. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 15 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Foreign Countries, College Faculty, Disproportionate Representation, Gender Bias, Racism, Ethnic Groups, Social Bias, Artificial Intelligence, Diversity (Faculty), Inclusion, Teacher Distribution, Visual Aids, Guidelines |
| Geographic Terms: | Australia |
| DOI: | 10.1080/0309877X.2025.2528788 |
| ISSN: | 0309-877X 1469-9486 |
| Abstract: | Generative artificial intelligence (AI) has the potential to be transformative or to amplify misrepresentations and biases. Generative AI text-to-image production using DALL-E 3 was evaluated for gender and ethnicity biases among Australian academics. DALL-E 3 produced multiple iterations of images using a variety of prompts. Collectively, 81 images were produced for evaluation of which 45 were individual characters and the remaining 36 images were comprised of multiple (7 to 66) characters. All images were independently analysed by three reviewers for apparent gender, age and skin tone (N = 967). 82.2% (N = 795) of academics were depicted as male and 94.2% (N = 911) as a light skin tone. The gender distribution is a statistically significant variation from that of actual Australian academics (p < 0.001). Generated images have a disproportionately high representation of white males as academics, which is not representative of the diversity of the university sector in Australia today. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1488628 |
| Database: | ERIC |
| Abstract: | Generative artificial intelligence (AI) has the potential to be transformative or to amplify misrepresentations and biases. Generative AI text-to-image production using DALL-E 3 was evaluated for gender and ethnicity biases among Australian academics. DALL-E 3 produced multiple iterations of images using a variety of prompts. Collectively, 81 images were produced for evaluation of which 45 were individual characters and the remaining 36 images were comprised of multiple (7 to 66) characters. All images were independently analysed by three reviewers for apparent gender, age and skin tone (N = 967). 82.2% (N = 795) of academics were depicted as male and 94.2% (N = 911) as a light skin tone. The gender distribution is a statistically significant variation from that of actual Australian academics (p < 0.001). Generated images have a disproportionately high representation of white males as academics, which is not representative of the diversity of the university sector in Australia today. |
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| ISSN: | 0309-877X 1469-9486 |
| DOI: | 10.1080/0309877X.2025.2528788 |
