Achieving Fairness in Determining Medicaid Eligibility through Fairgroup Construction
This work addresses fairness in Medicaid eligibility determination, which is a domain-specific problem for social welfare systems, and is incremental as it adapts existing classifiers.
The paper tackles the problem of unfair automated Medicaid eligibility decisions by proposing fairgroup construction, a method that improves fairness in regressive classifiers while maintaining high accuracy, as demonstrated on the American Community Survey dataset.
Effective complements to human judgment, artificial intelligence techniques have started to aid human decisions in complicated social problems across the world. In the context of United States for instance, automated ML/DL classification models offer complements to human decisions in determining Medicaid eligibility. However, given the limitations in ML/DL model design, these algorithms may fail to leverage various factors for decision making, resulting in improper decisions that allocate resources to individuals who may not be in the most need. In view of such an issue, we propose in this paper the method of \textit{fairgroup construction}, based on the legal doctrine of \textit{disparate impact}, to improve the fairness of regressive classifiers. Experiments on American Community Survey dataset demonstrate that our method could be easily adapted to a variety of regressive classification models to boost their fairness in deciding Medicaid Eligibility, while maintaining high levels of classification accuracy.