AI Driven Equity Analytics in New York City Home Care Workforce Management: Identifying Bias in Scheduling, Case Assignment, Workload Distribution, and Career Advancement

Abstract

New York City is experiencing growing issues with home care workforce management involving scheduling fairness, workload imbalance, unequal case assignments, and lack of transparency about career advancement opportunities. The problems lead to employee dissatisfaction, organizational bias, and less operational efficiency. Artificial intelligence (AI)-driven equity analytics has emerged as a promising solution for identifying workplace disparities and supporting fair workforce management practices. This study aimed to explore how AI-driven equity analytics can help uncover bias in the scheduling, case assignment, workload distribution, and career progression processes in NYC home care organizations. This study also investigated employee attitudes about AI adoption, trust in the organization, and ethical issues related to AI implementation.

The study design used was quantitative in which a structured close ended questionnaire was sent to 275 health care workers working in home care organizations in New York City. Relevant responses were gathered using purposive sampling approach so as to obtain responses of Home Health Aides, Nurses, Case Managers, Supervisors and Administrative Staff. The data was evaluated using descriptive statistics, reliability assessment, mean scores, standard deviations and Chi-Square tests in an intention to evaluate the perception of workforce equity and AI acceptance.

The findings revealed moderate perceptions of equity in scheduling (M = 3.58), equity in case assignment (M = 3.42), workload distribution (M = 3.35), and career advancement (M = 3.22) and these results indicated the persistence of issues of favouritism, workload distribution and lack of transparency in promotion in home care organizations. Instead, AI Equity Analytics had the highest mean score (M = 4.10), which indicates high levels of acceptance of AI-based tools to reduce bias and improve the fairness of the workforce. Problems of trust and ethics also became a relevant factor (M = 3.89), in particular, the problem of privacy, openness, and the necessity of the human control of AI-assisted decision-making. The Chi-Square values of all the study variables were found to be statistically significant (p < 0.05) indicating meaningful perceptions of the respondents regarding workforce equity and AI use.

The paper concludes that this AI-based equity analytics system has great potential to increase fairness, transparency and efficiency of home care worker management systems. Nevertheless, to be implemented efficiently, ethical leadership, trust among employees, privacy protection, and human control are vital to guarantee responsible and transparent AI-based decision-making.

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