改进查询推荐，让搜索更公平
A Case Study of Balanced Query Recommendation on Wikipedia
- 用帕累托前沿优化多维度公平性与相关性
- 在维基百科数据集上显著降低性别和地域偏见
- 适合关注算法公平性的研究者和开发者
现代信息检索系统是获取信息的重要工具。除搜索外，还包含查询扩展、查询推荐等重写方法以提升结果质量。然而，这些方法返回的结果有时会表现出针对性别、种族等受保护类别的不当或错误偏差。我们早期工作提出了平衡查询推荐（BalancedQR），目标不是基于公平性度量对结果列表进行重排序，而是建议与用户查询相关但偏差更小的查询。本文通过一个案例研究，展示了扩展版平衡查询推荐框架在处理多维度偏差上的有效性。该方法采用帕累托前沿策略，同时优化性别偏差、区域偏差以及检索结果的相关性。我们在维基百科数据集上评估了该扩展框架，结果表明其能有效减少多种类型偏见，并凸显出细微查询措辞与语言选择对检索效果的显著影响。
原文摘要 · Abstract (English)
Modern IR systems are an extremely important tool for seeking information. In addition to search, such systems include a number of query reformulation methods, such as query expansion and query recommendations, to provide high quality results. However, results returned by such methods sometimes exhibit undesirable or wrongful bias with respect to protected categories such as gender or race. Our earlier work considered the problem of balanced query recommendation, where instead of re-ranking a list of results based on fairness measures, the goal was to suggest queries that are relevant to a user's search query but exhibit less bias than the original query. In this work, we present a case study of BalancedQR using an extension of BalancedQR that handles biases in multiple dimensions. It employs a Pareto front approach that finds balanced queries, optimizing for multiple objectives such as gender bias and regional bias, along with the relevance of returned results. We evaluate the extended version of BalancedQR on a Wikipedia dataset.Our results demonstrate the effectiveness of our extension to BalancedQR framework and highlight the significant impact of subtle query wording,linguistic choice on retrieval.
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