提出感知意识指标，更准确检测搜索建议中的人名相关偏见
Perception-Aware Bias Detection for Query Suggestions
- 引入感知意识度量，应对查询建议稀疏与短暂呈现的挑战
- 实测发现该方法能有效识别用户可察觉的系统性主题偏见
- 适合关注搜索公平性与人名搜索推荐的研究者使用
网络搜索中的偏见问题长期受到关注，但查询建议的偏见研究仍不足。现有方法难以应对查询建议数据稀疏、缺乏上下文元信息的问题，且其呈现时间极短、用户感知隐晦。本文在Bonart等人提出的个人相关搜索查询建议偏见检测框架基础上，引入感知意识度量以克服上述困难。通过增强后的检测流程，能够更有效地识别出用户实际可能察觉的系统性主题偏见。基于该流程的分析结果验证了其有效性，表明所生成的发现更贴近真实用户感知。
原文摘要 · Abstract (English)
Bias in web search has been in the spotlight of bias detection research for quite a while. At the same time, little attention has been paid to query suggestions in this regard. Awareness of the problem of biased query suggestions has been raised. Likewise, there is a rising need for automatic bias detection approaches. This paper adds on the bias detection pipeline for bias detection in query suggestions of person-related search developed by Bonart et al. \cite{Bonart_2019a}. The sparseness and lack of contextual metadata of query suggestions make them a difficult subject for bias detection. Furthermore, query suggestions are perceived very briefly and subliminally. To overcome these issues, perception-aware metrics are introduced. Consequently, the enhanced pipeline is able to better detect systematic topical bias in search engine query suggestions for person-related searches. The results of an analysis performed with the developed pipeline confirm this assumption. Due to the perception-aware bias detection metrics, findings produced by the pipeline can be assumed to reflect bias that users would discern.
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