This paper provides an overview of the NIST TREC 2020 Fair Ranking track. For 2020, we again adopted an academic search task, where we have a corpus of academic article abstracts and queries submitted to a production academic search engine. The central goal of the Fair Ranking track is to provide fair exposure to different groups of authors (a group fairness framing). We recognize that there may be multiple group definitions (e.g. based on demographics, stature, topic) and hoped for the systems to be robust to these. We expected participants to develop systems that optimize for fairness and relevance for arbitrary group definitions, and did not reveal the exact group definitions until after the evaluation runs were submitted.The track contains two tasks,reranking and retrieval, with a shared evaluation.
翻译:本文概述了NIST TREC 2020 公平评分轨道。 2020 年,我们再次通过了一项学术搜索任务,我们在该任务中向生产型学术搜索引擎提交了大量学术文章摘要和询问。 公平评分轨道的中心目标是向不同作者群体提供公平曝光的机会(集体公平框架 ) 。 我们认识到,可能存在多种群体定义(例如基于人口统计、地位、主题),并希望这些系统能够对这些定义保持稳健。 我们期望参与者制定系统,优化任意群体定义的公平和相关性,并在提交评估报告之前不披露确切的团体定义。 该轨道包含两项任务,即重新排序和检索,并共同进行评估。