语言与文本到图像模型中的宗教偏见图景：分析、检测与去偏策略
Religious Bias Landscape in Language and Text-to-Image Models: Analysis, Detection, and Debiasing Strategies
AI总结：
本文系统分析语言模型和文本到图像模型中的宗教偏见，通过约400个自然提示发现显著刻板印象及跨领域偏见，并评估纠正性提示的去偏效果，呼吁开发更公平的模型。
摘要：
注意：本文包含可能与宗教偏见相关的潜在冒犯性内容示例，仅用于学术目的。语言模型的广泛采用凸显了对其固有偏见进行批判性审视的必要性，尤其是在宗教方面。本研究系统地调查了语言模型和文本到图像生成模型中的宗教偏见，同时分析了开源和闭源系统。我们构建了约400个独特的、自然出现的提示，以探究语言模型在多种任务中的宗教偏见，包括掩码填充、提示补全和图像生成。我们的实验揭示了令人担忧的潜在刻板印象和偏见实例，这些偏见不成比例地与某些宗教相关联。此外，我们探索了跨领域偏见，考察宗教偏见如何与性别、年龄和国籍等人口统计因素相交织。本研究进一步通过采用旨在减轻已识别偏见的纠正性提示，评估了针对性去偏技术的有效性。我们的研究结果表明，语言模型在文本和图像生成任务中继续表现出显著偏见，强调了开发更公平的语言模型以实现全球可接受性的迫切需要。
英文摘要：
Note: This paper includes examples of potentially offensive content related to religious bias, presented solely for academic purposes. The widespread adoption of language models highlights the need for critical examinations of their inherent biases, particularly concerning religion. This study systematically investigates religious bias in both language models and text-to-image generation models, analyzing both open-source and closed-source systems. We construct approximately 400 unique, naturally occurring prompts to probe language models for religious bias across diverse tasks, including mask filling, prompt completion, and image generation. Our experiments reveal concerning instances of underlying stereotypes and biases associated disproportionately with certain religions. Additionally, we explore cross-domain biases, examining how religious bias intersects with demographic factors such as gender, age, and nationality. This study further evaluates the effectiveness of targeted debiasing techniques by employing corrective prompts designed to mitigate the identified biases. Our findings demonstrate that language models continue to exhibit significant biases in both text and image generation tasks, emphasizing the urgent need to develop fairer language models to achieve global acceptability.
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