Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairness
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918171314749440 |
|---|---|
| author | Simson, Jan Fabris, Alessandro Fröhner, Cosima Kreuter, Frauke Kern, Christoph |
| author_facet | Simson, Jan Fabris, Alessandro Fröhner, Cosima Kreuter, Frauke Kern, Christoph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairness Simson, Jan Fabris, Alessandro Fröhner, Cosima Kreuter, Frauke Kern, Christoph Machine Learning Computers and Society 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. |
| title | Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairness |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2510.22363 |