Text-to-Image Models Prompted on Intellect

Citation Author(s):
Juliana
Shihadeh
Submitted by:
Juliana Shihadeh
Last updated:
Wed, 10/23/2024 - 16:52
DOI:
10.21227/c5ym-kw44
Data Format:
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Abstract 

Text-to-image models, like Midjourney and DALL-E, have been shown to reinforce harmful biases, often perpetuating outdated and discriminatory stereotypes. In this study, we delve into a particularly insidious bias largely overlooked in generative image research: Brilliance Bias. By age six, many children begin to internalize the damaging notion that intellectual brilliance is a male trait—a belief that persists into adulthood. Our findings demonstrate that popular image AI models possess this bias, further entrenching the misguided notion that exceptional intelligence is inherently male. This study calls for addressing these biases in AI to ensure a more realistic representation of intellectual capabilities, helping shape a future where talent and brilliance are more broadly recognized.

Instructions: 

In this dataset the images are organized into folders based on the model and the prompt that generated them. 

Comments

Info about the dataset. The data will be added before the paper is published

Submitted by Juliana Shihadeh on Wed, 10/23/2024 - 16:45

Dataset Files

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