Implications of algorithmic bias in AI-driven emergency response systems

Abstract

In this paper, we introduce a framework to evaluate the economic implications of algorithmic bias specifically for the emergency response systems that incorporate AI. Unlike existing research, which mostly addresses technical or ethical aspects in isolation, our approach integrates economic theory with algorithmic fairness to quantify and systematically analyze how biases in data quality and algorithm design impact resource allocation efficiency, response time equity, healthcare outcomes, and social welfare. Using explicit modeling of emergency-specific variables, which includes time sensitivity and urgency, we demonstrate that biases substantially exacerbate demographic disparities. This could lead to delayed emergency responses, inefficient resource utilization, worsened health outcomes, and significant welfare losses. Our numerical simulations further illustrate the economic viability and effectiveness of bias mitigation strategies, such as fairness-constrained optimization and improved data representativeness, in simultaneously enhancing equity and economic efficiency. The framework presented provides policymakers, healthcare providers, and AI developers with actionable insights and a robust economic rationale for deploying equitable AI-driven solutions in emergency management contexts.