When interacting with information retrieval (IR) systems, users, affected by confirmation biases, tend to select search results that confirm their existing beliefs on socially significant contentious issues. To understand the judgments and attitude changes of users searching online, our study examined how cognitively biased users interact with algorithmically biased search engine result pages (SERPs). We designed three-query search sessions on debated topics under various bias conditions. We recruited 1,321 crowdsourcing participants and explored their attitude changes, search interactions, and the effects of confirmation bias. Three key findings emerged: 1) most attitude changes occur in the initial query of a search session; 2) Confirmation bias and result presentation on SERPs affect the number and depth of clicks in the current query and perceived familiarity with clicked results in subsequent queries; 3) The bias position also affects attitude changes of users with lower perceived openness to conflicting opinions. Our study goes beyond traditional simulation-based evaluation settings and simulated rational users, sheds light on the mixed effects of human biases and algorithmic biases in information retrieval tasks on debated topics, and can inform the design of bias-aware user models, human-centered bias mitigation techniques, and socially responsible intelligent IR systems.

翻译：当用户与信息检索系统交互时，受确认偏差影响，他们倾向于选择那些证实其对社会重大争议议题既有信念的搜索结果。为理解在线搜索中用户的判断与态度变化，本研究考察了具有认知偏差的用户如何与存在算法偏差的搜索引擎结果页面交互。我们设计了在有争议话题下、多种偏差条件下的三查询搜索会话。通过招募1,321名众包参与者，我们探究了其态度变化、搜索交互行为以及确认偏差的影响。研究得出三个关键发现：1）大多数态度变化发生在搜索会话的初始查询阶段；2）确认偏差与搜索结果页面的呈现方式会影响当前查询的点击数量与深度，以及后续查询中对已点击结果的感知熟悉度；3）偏差位置也会影响那些自认为对冲突观点开放度较低的用户的态度变化。本研究超越了传统基于模拟的评估设置与理性用户假设，揭示了在有争议话题的信息检索任务中人类偏差与算法偏差的混合效应，可为偏差感知用户模型、以人为中心的偏差缓解技术以及具有社会责任的智能信息检索系统的设计提供参考。