Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairness

TL;DR

This paper introduces FairGround, a comprehensive dataset collection and toolkit designed to improve the reproducibility, diversity, and fairness of machine learning research by providing standardized, annotated datasets and processing tools.

Contribution

The paper presents FairGround, a unified framework with 44 diverse datasets and a Python package to standardize fair ML research workflows and enhance reproducibility.

Findings

Provides a diverse, annotated dataset corpus for fair ML research

Standardizes data processing and experimental workflows

Supports open, collaborative, and reproducible fair ML studies

Abstract

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.