Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairness
This addresses the need for more robust and reproducible research in fair machine learning, particularly for high-stakes decision-making domains, though it is incremental as it builds on existing data collection efforts.
The paper tackles the problem of limited and inconsistent datasets in algorithmic fairness research by introducing FairGround, a unified framework and corpus of 44 tabular datasets with fairness metadata and a Python package, which standardizes workflows to improve reproducibility and generalizability.
As machine learning (ML) systems are increasingly adopted in high-stakes decision-making domains, ensuring fairness in their outputs has become a central challenge. At the core of fair ML research are the datasets used to investigate bias and develop mitigation strategies. Yet, much of the existing work relies on a narrow selection of datasets--often arbitrarily chosen, inconsistently processed, and lacking in diversity--undermining the generalizability and reproducibility of results. To address these limitations, we present FairGround: a unified framework, data corpus, and Python package aimed at advancing reproducible research and critical data studies in fair ML classification. FairGround currently comprises 44 tabular datasets, each annotated with rich fairness-relevant metadata. Our accompanying Python package standardizes dataset loading, preprocessing, transformation, and splitting, streamlining experimental workflows. By providing a diverse and well-documented dataset corpus along with robust tooling, FairGround enables the development of fairer, more reliable, and more reproducible ML models. All resources are publicly available to support open and collaborative research.