Exploring the Economic Impacts of Algorithmic Bias in AI-Based Healthcare Systems

تحليل الآثار الاقتصادية للتحيز الخوارزمي في أنظمة الرعاية الصحية المعتمدة على الذكاء الاصطناعي

Abstract

Abstract

     The COVID-19 pandemic demonstrated the dismal insufficiency of the whole healthcare system in all countries, including the resource distribution and the diagnostic cycle. Another important but underemphasized concern was the presence of algorithmic biases, coded in the crisis of emergency interventions, with a negative implication on already existing disparities in health, and an exponential contribution to the impact of the crisis itself. All of these forms of biases were overrepresented in the vulnerable populations, resulting in poor health outcomes and incurring high economic costs, primarily in triage algorithms to allocate scarce resources. This paper offers the general model of the economic effect of algorithmic bias in the COVID-19 pandemic, both directly (the health care spending, hospitalization, intensive care unit), and indirectly (e.g., the loss of productivity due to the excess number of fatalities). The study measures the extent to which biases contributed to the global economy in two different conditions: mild (5%) and severe (15%) by simulating the two conditions, and the results are used to determine the impact of the bias on economic stability at the country and global levels. According to the results, the presence of algorithmic bias, especially in high-income countries, is one of the contributors to a large portion of the loss of money, as supported by the fact that in the vast majority, the indirect expense of excess death and loss of productivity. The conclusion of the paper provides practical policy suggestions as to how to reduce these economic predispositions in the event of future health crises, but more equitable, transparent, and accountable emergency response mechanisms are required.