检测大模型生成广告中的性别、年龄等偏见，揭示算法不平等现象
Towards Equitable AI: Detecting Bias in Using Large Language Models for Marketing
- 用特定人群提示生成1700条广告语，分析主题差异
- 女性、低收入者等群体获更明显差异化营销信息
- 提出可操作的偏见检测框架，适合伦理与营销研究者
大语言模型（LLMs）在金融、营销等领域广泛应用，但其输出可能嵌入性别、年龄等社会偏见，引发公平性问题。本研究通过提示词针对五类人口统计特征（性别、婚姻状况、年龄、收入水平、教育程度）生成1700条金融营销标语，覆盖17个独特群体。关键术语被归类为四大主题：赋能、财务、利益与特性、个性化。采用相对偏见计算和柯尔莫哥洛夫-斯米尔诺夫（KS）检验，对比通用标语。结果表明，营销文案并非中立，不同群体强调的主题存在显著差异。女性、年轻个体、低收入及低学历者获得更突出的差异化信息，而年长、高收入、高学历者则更为一致。研究揭示了生成式AI在营销中潜在的系统性偏见，呼吁建立持续的偏见检测与缓解机制。
原文摘要 · Abstract (English)
The recent advances in large language models (LLMs) have revolutionized industries such as finance, marketing, and customer service by enabling sophisticated natural language processing tasks. However, the broad adoption of LLMs brings significant challenges, particularly in the form of social biases that can be embedded within their outputs. Biases related to gender, age, and other sensitive attributes can lead to unfair treatment, raising ethical concerns and risking both company reputation and customer trust. This study examined bias in finance-related marketing slogans generated by LLMs (i.e., ChatGPT) by prompting tailored ads targeting five demographic categories: gender, marital status, age, income level, and education level. A total of 1,700 slogans were generated for 17 unique demographic groups, and key terms were categorized into four thematic groups: empowerment, financial, benefits and features, and personalization. Bias was systematically assessed using relative bias calculations and statistically tested with the Kolmogorov-Smirnov (KS) test against general slogans generated for any individual. Results revealed that marketing slogans are not neutral; rather, they emphasize different themes based on demographic factors. Women, younger individuals, low-income earners, and those with lower education levels receive more distinct messaging compared to older, higher-income, and highly educated individuals. This underscores the need to consider demographic-based biases in AI-generated marketing strategies and their broader societal implications. The findings of this study provide a roadmap for developing more equitable AI systems, highlighting the need for ongoing bias detection and mitigation efforts in LLMs.
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