分析AI应急系统偏见的经济代价，揭示其对资源分配与社会福利的负面影响。
Modelling of Economic Implications of Bias in AI-Powered Health Emergency Response Systems
- 构建融合健康与福利经济学的理论框架，量化算法偏见影响。
- 偏见导致资源错配、成本上升和福利损失，加剧效率与公平矛盾。
- 适合政策制定者、医疗应急机构及科技开发者参考应对偏见。
我们提出一个理论框架，评估人工智能驱动的应急响应系统中偏见的经济影响。结合健康经济学、福利经济学与人工智能，分析算法偏见如何影响资源分配、健康结果与社会福利。通过在健康生产函数与社会福利模型中引入偏见函数，量化其对不同人口群体的影响，发现偏见导致资源配置次优、成本增加及福利损失。该框架揭示了效率与公平之间的权衡，并提供经济解释。我们提出公平约束优化、算法调整与政策干预等缓解策略。研究为政策制定者、应急服务机构及技术开发者提供洞见，强调在应急响应中实现高效与公平的必要性。通过应对偏见的经济后果，本研究推动更公平、高效与促进社会福利的应急技术与政策发展。
原文摘要 · Abstract (English)
We present a theoretical framework assessing the economic implications of bias in AI-powered emergency response systems. Integrating health economics, welfare economics, and artificial intelligence, we analyze how algorithmic bias affects resource allocation, health outcomes, and social welfare. By incorporating a bias function into health production and social welfare models, we quantify its impact on demographic groups, showing that bias leads to suboptimal resource distribution, increased costs, and welfare losses. The framework highlights efficiency-equity trade-offs and provides economic interpretations. We propose mitigation strategies, including fairness-constrained optimization, algorithmic adjustments, and policy interventions. Our findings offer insights for policymakers, emergency service providers, and technology developers, emphasizing the need for AI systems that are efficient and equitable. By addressing the economic consequences of biased AI, this study contributes to policies and technologies promoting fairness, efficiency, and social welfare in emergency response services.
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