用复杂提示词探测图像生成中的细微偏见，揭示数据与社会双重偏见。
Using complex prompts to identify fine-grained biases in image generation through ChatGPT-4o
- 通过设计复杂提示词，挖掘图像生成中隐含的细微偏见。
- 发现模型会过度强调社会标记特征，放大现实中的不平等现象。
- 适合关注AI伦理、社会偏见检测的研究者和实践者。
研究大型AI模型时可揭示两类偏见：一是训练数据或生成结果中的偏见，二是社会层面的偏见，如不同人口群体在就业或健康结果上的差异。图像数据集常过度代表某些群体（如年长白人），而大模型有时也准确反映现实偏见（如年轻黑人男性被过度视为威胁）。这些社会不平等常以“标记特征”形式出现在图像生成中，即某些个体或场景特征成为社会差异的符号，引发人类与AI对特定群体的特殊对待。生成式AI对这类标记特征极为敏感，甚至可能加剧偏见。本文简要探讨如何利用复杂提示词探究这两类偏见，强调可通过自动化情感分析等手段分析用于生成图像的文本提示，进而探查图像生成背后的大语言模型行为。
原文摘要 · Abstract (English)
There are not one but two dimensions of bias that can be revealed through the study of large AI models: not only bias in training data or the products of an AI, but also bias in society, such as disparity in employment or health outcomes between different demographic groups. Often training data and AI output is biased for or against certain demographics (i.e. older white people are overrepresented in image datasets), but sometimes large AI models accurately illustrate biases in the real world (i.e. young black men being disproportionately viewed as threatening). These social disparities often appear in image generation AI outputs in the form of 'marked' features, where some feature of an individual or setting is a social marker of disparity, and prompts both humans and AI systems to treat subjects that are marked in this way as exceptional and requiring special treatment. Generative AI has proven to be very sensitive to such marked features, to the extent of over-emphasising them and thus often exacerbating social biases. I briefly discuss how we can use complex prompts to image generation AI to investigate either dimension of bias, emphasising how we can probe the large language models underlying image generation AI through, for example, automated sentiment analysis of the text prompts used to generate images.
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