A Case Study of Balanced Query Recommendation on Wikipedia

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.