seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866915236938776576 |
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| author | Ning, Yilin Ma, Yian Liu, Mingxuan Li, Xin Liu, Nan |
| author_facet | Ning, Yilin Ma, Yian Liu, Mingxuan Li, Xin Liu, Nan |
| contents | Fairness in artificial intelligence (AI) prediction models is increasingly emphasized to support responsible adoption in high-stakes domains such as health care and criminal justice. Guidelines and implementation frameworks highlight the importance of both predictive accuracy and equitable outcomes. However, current fairness toolkits often evaluate classification performance disparities in isolation, with limited attention to other critical aspects such as calibration. To address these gaps, we present seeBias, an R package for comprehensive evaluation of model fairness and predictive performance. seeBias offers an integrated evaluation across classification, calibration, and other performance domains, providing a more complete view of model behavior. It includes customizable visualizations to support transparent reporting and responsible AI implementation. Using public datasets from criminal justice and healthcare, we demonstrate how seeBias supports fairness evaluations, and uncovers disparities that conventional fairness metrics may overlook. The R package is available on GitHub, and a Python version is under development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08418 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness Ning, Yilin Ma, Yian Liu, Mingxuan Li, Xin Liu, Nan Machine Learning Artificial Intelligence Fairness in artificial intelligence (AI) prediction models is increasingly emphasized to support responsible adoption in high-stakes domains such as health care and criminal justice. Guidelines and implementation frameworks highlight the importance of both predictive accuracy and equitable outcomes. However, current fairness toolkits often evaluate classification performance disparities in isolation, with limited attention to other critical aspects such as calibration. To address these gaps, we present seeBias, an R package for comprehensive evaluation of model fairness and predictive performance. seeBias offers an integrated evaluation across classification, calibration, and other performance domains, providing a more complete view of model behavior. It includes customizable visualizations to support transparent reporting and responsible AI implementation. Using public datasets from criminal justice and healthcare, we demonstrate how seeBias supports fairness evaluations, and uncovers disparities that conventional fairness metrics may overlook. The R package is available on GitHub, and a Python version is under development. |
| title | seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.08418 |