用大模型对比分析谷歌与必应政治搜索建议的偏见
Investigating Bias in Political Search Query Suggestions by Relative Comparison with LLMs
- 通过大模型配对比较和埃洛评分量化偏见
- 发现谷歌与必应的政治建议存在显著差异
- 适合关注信息公平性的研究者与平台方
搜索查询建议影响用户与搜索引擎的互动，进而影响其接触到的信息，因此搜索建议中的偏见可能导致偏向性结果暴露，并影响观点形成，尤其在政治领域尤为关键。由于主题依赖性、复杂性和主观性，检测和量化网络搜索引擎中的偏见极具挑战性，而查询建议的缺乏上下文和短语特性加剧了这一问题。本文提出一种多步骤方法，结合大语言模型、成对比较和基于埃洛的评分机制，识别并量化英文搜索建议中的偏见。我们将在美国政治新闻领域应用该方法，比较谷歌与必应的偏见情况。
原文摘要 · Abstract (English)
Search query suggestions affect users' interactions with search engines, which then influences the information they encounter. Thus, bias in search query suggestions can lead to exposure to biased search results and can impact opinion formation. This is especially critical in the political domain. Detecting and quantifying bias in web search engines is difficult due to its topic dependency, complexity, and subjectivity. The lack of context and phrasality of query suggestions emphasizes this problem. In a multi-step approach, we combine the benefits of large language models, pairwise comparison, and Elo-based scoring to identify and quantify bias in English search query suggestions. We apply our approach to the U.S. political news domain and compare bias in Google and Bing.
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