Bias by Design? How Data Practices Shape Fairness in AI Healthcare Systems
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911228148842496 |
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| author | Arias-Duart, Anna Cardello, Maria Eugenia Cortés, Atia |
| author_facet | Arias-Duart, Anna Cardello, Maria Eugenia Cortés, Atia |
| contents | Artificial intelligence (AI) holds great promise for transforming healthcare. However, despite significant advances, the integration of AI solutions into real-world clinical practice remains limited. A major barrier is the quality and fairness of training data, which is often compromised by biased data collection practices. This paper draws on insights from the AI4HealthyAging project, part of Spain's national R&D initiative, where our task was to detect biases during clinical data collection. We identify several types of bias across multiple use cases, including historical, representation, and measurement biases. These biases manifest in variables such as sex, gender, age, habitat, socioeconomic status, equipment, and labeling. We conclude with practical recommendations for improving the fairness and robustness of clinical problem design and data collection. We hope that our findings and experience contribute to guiding future projects in the development of fairer AI systems in healthcare. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20332 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bias by Design? How Data Practices Shape Fairness in AI Healthcare Systems Arias-Duart, Anna Cardello, Maria Eugenia Cortés, Atia Artificial Intelligence Artificial intelligence (AI) holds great promise for transforming healthcare. However, despite significant advances, the integration of AI solutions into real-world clinical practice remains limited. A major barrier is the quality and fairness of training data, which is often compromised by biased data collection practices. This paper draws on insights from the AI4HealthyAging project, part of Spain's national R&D initiative, where our task was to detect biases during clinical data collection. We identify several types of bias across multiple use cases, including historical, representation, and measurement biases. These biases manifest in variables such as sex, gender, age, habitat, socioeconomic status, equipment, and labeling. We conclude with practical recommendations for improving the fairness and robustness of clinical problem design and data collection. We hope that our findings and experience contribute to guiding future projects in the development of fairer AI systems in healthcare. |
| title | Bias by Design? How Data Practices Shape Fairness in AI Healthcare Systems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.20332 |