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.

翻译：网络搜索中的偏见长期以来一直是偏见检测研究的焦点。与此同时，查询建议在这一领域却鲜少受到关注。人们已逐渐认识到查询建议存在偏见的问题。同样，对自动偏见检测方法的需求也在日益增长。本文基于Bonart等人\cite{Bonart_2019a}为人物相关搜索的查询建议偏见检测所开发的检测流程进行了扩展。查询建议的稀疏性及上下文元数据的缺乏，使其成为偏见检测的难题。此外，用户对查询建议的感知极为短暂且多处于潜意识层面。为克服这些问题，本文引入了感知感知的度量指标。因此，增强后的流程能够更有效地检测人物相关搜索中搜索引擎查询建议的系统性主题偏见。利用所开发流程进行分析的结果证实了这一假设。得益于感知感知的偏见检测指标，该流程所发现的偏见可被假定为用户能够察觉的偏见。