Bias in Generative AI

• URL: http://arxiv.org/abs/2403.02726v1
• Date: Tue, 5 Mar 2024 07:34:41 GMT
• Title: Bias in Generative AI
• Authors: Mi Zhou, Vibhanshu Abhishek, Timothy Derdenger, Jaymo Kim, Kannan Srinivasan
• Abstract summary: This study analyzed images generated by three popular generative artificial intelligence (AI) tools to investigate potential bias in AI generators. All three AI generators exhibited bias against women and African Americans. Women were depicted as younger with more smiles and happiness, while men were depicted as older with more neutral expressions and anger.
• Score: 2.5830293457323266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study analyzed images generated by three popular generative artificial intelligence (AI) tools - Midjourney, Stable Diffusion, and DALLE 2 - representing various occupations to investigate potential bias in AI generators. Our analysis revealed two overarching areas of concern in these AI generators, including (1) systematic gender and racial biases, and (2) subtle biases in facial expressions and appearances. Firstly, we found that all three AI generators exhibited bias against women and African Americans. Moreover, we found that the evident gender and racial biases uncovered in our analysis were even more pronounced than the status quo when compared to labor force statistics or Google images, intensifying the harmful biases we are actively striving to rectify in our society. Secondly, our study uncovered more nuanced prejudices in the portrayal of emotions and appearances. For example, women were depicted as younger with more smiles and happiness, while men were depicted as older with more neutral expressions and anger, posing a risk that generative AI models may unintentionally depict women as more submissive and less competent than men. Such nuanced biases, by their less overt nature, might be more problematic as they can permeate perceptions unconsciously and may be more difficult to rectify. Although the extent of bias varied depending on the model, the direction of bias remained consistent in both commercial and open-source AI generators. As these tools become commonplace, our study highlights the urgency to identify and mitigate various biases in generative AI, reinforcing the commitment to ensuring that AI technologies benefit all of humanity for a more inclusive future.

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