2019/06/01 by Boli Fang, Fang, Boli, Miao Jiang +3
Computer Science · Mathematics · Social Sciences · #Actuarial science #Artificial Intelligence (cs.AI) #Artificial intelligence #Business #Computer science #Computers and Society (cs.CY) #Context (archaeology) #Data science #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Law #Leverage (statistics) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Medicaid #Political science #Variety (cybernetics) #cs.AI #cs.CY #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1906.00128
arxiv created 2019/06/01 · openalex publication_date 2019/06/01 · arxiv updated 2019/06/04 · openalex created_date 2019/06/07 · openalex updated_date 2026/07/28
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 fairgroup construction, based on the legal doctrine of 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.