Fair Machine Learning for Healthcare Requires Recognizing the Intersectionality of Sociodemographic Factors, a Case Study
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| Format: | Preprint |
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2024
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| author | Valentine, Alissa A. Charney, Alexander W. Landi, Isotta |
| author_facet | Valentine, Alissa A. Charney, Alexander W. Landi, Isotta |
| contents | As interest in implementing artificial intelligence (AI) in medical systems grows, discussion continues on how to evaluate the fairness of these systems, or the disparities they may perpetuate. Socioeconomic status (SES) is commonly included in machine learning models to control for health inequities, with the underlying assumption that increased SES is associated with better health. In this work, we considered a large cohort of patients from the Mount Sinai Health System in New York City to investigate the effect of patient SES, race, and sex on schizophrenia (SCZ) diagnosis rates via a logistic regression model. Within an intersectional framework, patient SES, race, and sex were found to have significant interactions. Our findings showed that increased SES is associated with a higher probability of obtaining a SCZ diagnosis in Black Americans ($β=4.1\times10^{-8}$, $SE=4.5\times10^{-9}$, $p < 0.001$). Whereas high SES acts as a protective factor for SCZ diagnosis in White Americans ($β=-4.1\times10^{-8}$, $SE=6.7\times10^{-9}$, $p < 0.001$). Further investigation is needed to reliably explain and quantify health disparities. Nevertheless, we advocate that building fair AI tools for the health care space requires recognizing the intersectionality of sociodemographic factors. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_15006 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fair Machine Learning for Healthcare Requires Recognizing the Intersectionality of Sociodemographic Factors, a Case Study Valentine, Alissa A. Charney, Alexander W. Landi, Isotta Computers and Society Machine Learning As interest in implementing artificial intelligence (AI) in medical systems grows, discussion continues on how to evaluate the fairness of these systems, or the disparities they may perpetuate. Socioeconomic status (SES) is commonly included in machine learning models to control for health inequities, with the underlying assumption that increased SES is associated with better health. In this work, we considered a large cohort of patients from the Mount Sinai Health System in New York City to investigate the effect of patient SES, race, and sex on schizophrenia (SCZ) diagnosis rates via a logistic regression model. Within an intersectional framework, patient SES, race, and sex were found to have significant interactions. Our findings showed that increased SES is associated with a higher probability of obtaining a SCZ diagnosis in Black Americans ($β=4.1\times10^{-8}$, $SE=4.5\times10^{-9}$, $p < 0.001$). Whereas high SES acts as a protective factor for SCZ diagnosis in White Americans ($β=-4.1\times10^{-8}$, $SE=6.7\times10^{-9}$, $p < 0.001$). Further investigation is needed to reliably explain and quantify health disparities. Nevertheless, we advocate that building fair AI tools for the health care space requires recognizing the intersectionality of sociodemographic factors. |
| title | Fair Machine Learning for Healthcare Requires Recognizing the Intersectionality of Sociodemographic Factors, a Case Study |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2407.15006 |