InfoSeeking Lab's FATE (Fairness Accountability Transparency Ethics) group at University of Washington participated in 2020 TREC Fairness Ranking Track. This report describes that track, assigned data and tasks, our group definitions, and our results. Our approach to bringing fairness in retrieval and re-ranking tasks with Semantic Scholar data was to extract various dimensions of author identity. These dimensions included gender and location. We developed modules for these extractions in a way that allowed us to plug them in for either of the tasks as needed. After trying different combinations of relative weights assigned to relevance, gender, and location information, we chose five runs for retrieval and five runs for re-ranking tasks. The results showed that our runs performed below par for re-ranking task, but above average for retrieval.
翻译:华盛顿大学InfoSeeking实验室FATE(公平问责透明道德)小组参加了2020年TREC公平排名轨道,本报告描述了该轨道、分配的数据和任务、我们集团的定义和我们的结果。我们利用语义学者数据实现检索和重排任务的公平性的方法是提取作者身份的不同层面。这些层面包括性别和位置。我们开发了这些提取模块,使我们得以根据需要将这些模块插入其中任何一个任务。在尝试将相关、性别和位置信息相对权重的不同组合后,我们选择了5次运行进行检索,5次运行进行重新排级任务。结果显示,我们完成的排量低于等值,但高于平均的排位任务,用于检索。