FairRankTune: A Python Toolkit for Fair Ranking Tasks

We present FairRankTune, a multi-purpose open-source Python toolkit offering three primary services: quantifying fairness-related harms, leveraging bias mitigation algorithms, and constructing custom fairness-relevant datasets. FairRankTune provides researchers and practitioners with a self-contained resource for fairness auditing, experimentation, and advancing research. The central piece of FairRankTune is a novel fairness-tunable ranked data generator, RankTune, that streamlines the creation of custom fairness-relevant ranked datasets. FairRankTune also offers numerous fair ranking metrics and fairness-aware ranking algorithms within the same plug-and-play package. We demonstrate the key innovations of FairRankTune, focusing on features that are valuable to stakeholders via use cases highlighting workflows in the end-to-end process of mitigating bias in ranking systems. FairRankTune addresses the gap of limited publicly available datasets, auditing tools, and implementations for fair ranking.