Within a few years up to 90% of online content might be AI generated [1]. Biases and stereotypes do not only exist in the “real” world, but are even stronger in data generated by AI models [2]. As the models “improve”, they often use the new data on the Internet, which includes more and more images generated by these models. Therefore the biases could grow stronger and stronger. To face this problem, in this MA thesis we look deeper into this matter and compare two of the most used text to image generators: Adobe Firefly/ DALL-E and Stable Diffusion.
Goal of this work is to calculate the biases of the tools with focus on skin color and gender. Therefore several images will be generated and analysed. The dependent variable is “Job”: As biases and disadvantages in jobs and payment are an everlasting topic, images of people with different professions (some high paying and some low paying) should be generated and their skin tone and gender should be analysed. Additionally the aspect “Family” shall be taken in concern: Images of Families/ Couples should be generated and analysed.
Bianchi, F., Kalluri, P., Durmus, E., Ladhak, F., Cheng, M., Nozza, D., … Caliskan, A. (2023, Juni). Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale. In 2023 ACM Conference on Fairness, Accountability, and Transparency (S. 1493–1504). Zugriff am 2024-06-06 auf http://arxiv.org/ abs/2211.03759 (arXiv:2211.03759 [cs]) doi: 10.1145/3593013.3594095
Cho, J., Zala, A. & Bansal, M. (2023, August). DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models. arXiv. Zugriff am 2024-06-06 auf http://arxiv.org/abs/2202.04053 (arXiv:2202.04053 [cs])
Fraser, K. C., Kiritchenko, S. & Nejadgholi, I. (2023, Februar). A Friendly Face: Do Text-to-Image Systems Rely on Stereotypes when the Input is Under-Specified? ar- Xiv. Zugriff am 2024-04-26 auf http://arxiv.org/abs/2302.07159 (ar- Xiv:2302.07159 [cs])
Jha, A., Prabhakaran, V., Denton, R., Laszlo, S., Dave, S., Qadri, R., … Dev, S. (2024, Februar). ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation. arXiv. Zugriff am 2024-03-25 auf http://arxiv.org/abs/2401 .06310 (arXiv:2401.06310 [cs]) doi: 10.48550/arXiv.2401.06310
Koch, A., Imhoff, R., Dotsch, R., Unkelbach, C. & Alves, H. (2016, Mai). The ABC of Stereotypes About Groups: Agency/Socioeconomic Success, Conservative- Progressive Beliefs, and Communion. Journal of personality and social psychology, 110, 675–709. doi: 10.1037/pspa0000046
Sandoval-Martin, T. & Martínez-Sanzo, E. (2024, Mai). Perpetuation of Gender Bias in Visual Representation of Professions in the Generative AI Tools DALL·E and Bing Image Creator. Social Sciences, 13 (5), 250. Zugriff am 2024-06-12 auf https://www.mdpi.com/2076-0760/13/5/250 (Number: 5 Publisher: Multidisciplinary Digital Publishing Institute) doi: 10.3390/socsci13050250