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<p>This research looked at a form of potential gender bias in user generated AI-images, focusing on how female subjects are often objectified. User-posted content from the “r/AiArt” subreddit, where AI-generated images are shared, was the focal point of analysis. Previous research has shown that AI-generated images frequently objectify women and girls, a problem linked to the biases in the data used to train these systems. This type of objectification can have real-world consequences, shaping how society views women and contributing to harmful mental health effects.</p><p>To investigate this further, this research analyzed 277 images from “r/AiArt,” which is known for a mix of content from different AI generators like DALL-E 3 and Midjourney. I used an image-captioning model to create descriptive captions for each image, then measured objectification by looking for keywords tied to body focus, suggestive content, and NSFW terms.</p><p>The Chi-Square Test revealed a significant association between gender and objectification (<i>p</i> &lt; 0.001), with 60% of the female images showing signs of objectification, compared to just 20% of male images. This supports the idea that AI-generated images often reflect and reinforce societal gender biases.</p>

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Through the AI looking glass: measuring gendered objectification in user-generated AI images

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Anusha Asim

摘要

This research looked at a form of potential gender bias in user generated AI-images, focusing on how female subjects are often objectified. User-posted content from the “r/AiArt” subreddit, where AI-generated images are shared, was the focal point of analysis. Previous research has shown that AI-generated images frequently objectify women and girls, a problem linked to the biases in the data used to train these systems. This type of objectification can have real-world consequences, shaping how society views women and contributing to harmful mental health effects.

To investigate this further, this research analyzed 277 images from “r/AiArt,” which is known for a mix of content from different AI generators like DALL-E 3 and Midjourney. I used an image-captioning model to create descriptive captions for each image, then measured objectification by looking for keywords tied to body focus, suggestive content, and NSFW terms.

The Chi-Square Test revealed a significant association between gender and objectification (p < 0.001), with 60% of the female images showing signs of objectification, compared to just 20% of male images. This supports the idea that AI-generated images often reflect and reinforce societal gender biases.