  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.
