Modelling of Economic Implications of Bias in AI-Powered Health Emergency Response Systems

• URL: http://arxiv.org/abs/2410.20229v1
• Date: Sat, 26 Oct 2024 17:11:23 GMT
• Title: Modelling of Economic Implications of Bias in AI-Powered Health Emergency Response Systems
• Authors: Katsiaryna Bahamazava,
• Abstract summary: We analyze how algorithmic bias affects resource allocation, health outcomes, and social welfare. We propose mitigation strategies, including fairness-constrained optimization, algorithmic adjustments, and policy interventions.
• Score: 0.0
• License:
• Abstract: 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.

Related papers

• Causal Responsibility Attribution for Human-AI Collaboration [62.474732677086855]
  This paper presents a causal framework using Structural Causal Models (SCMs) to systematically attribute responsibility in human-AI systems. Two case studies illustrate the framework's adaptability in diverse human-AI collaboration scenarios.
  arXiv  Detail & Related papers  (2024-11-05T17:17:45Z)
• Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
  In practice, causal researchers do not have a single outcome in mind a priori. In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty. We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
  arXiv  Detail & Related papers  (2024-04-29T08:16:30Z)
• Causal Fairness for Outcome Control [68.12191782657437]
  We study a specific decision-making task called outcome control in which an automated system aims to optimize an outcome variable $Y$ while being fair and equitable. In this paper, we first analyze through causal lenses the notion of benefit, which captures how much a specific individual would benefit from a positive decision. We then note that the benefit itself may be influenced by the protected attribute, and propose causal tools which can be used to analyze this.
  arXiv  Detail & Related papers  (2023-06-08T09:31:18Z)
• Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies [11.323961700172175]
  This survey paper offers a succinct, comprehensive overview of fairness and bias in AI. We review sources of bias, such as data, algorithm, and human decision biases. We assess the societal impact of biased AI systems, focusing on the perpetuation of inequalities and the reinforcement of harmful stereotypes.
  arXiv  Detail & Related papers  (2023-04-16T03:23:55Z)
• Towards a Fairness-Aware Scoring System for Algorithmic Decision-Making [35.21763166288736]
  We propose a general framework to create data-driven fairness-aware scoring systems. We show that the proposed framework provides practitioners or policymakers great flexibility to select their desired fairness requirements.
  arXiv  Detail & Related papers  (2021-09-21T09:46:35Z)
• Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist [67.08543240320756]
  We show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning and data-driven simulations. We find that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes.
  arXiv  Detail & Related papers  (2021-08-06T01:30:41Z)
• The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning [126.37520136341094]
  We show that machine-learning-based economic simulation is a powerful policy and mechanism design framework. The AI Economist is a two-level, deep RL framework that trains both agents and a social planner who co-adapt. In simple one-step economies, the AI Economist recovers the optimal tax policy of economic theory.
  arXiv  Detail & Related papers  (2021-08-05T17:42:35Z)
• Fair Influence Maximization: A Welfare Optimization Approach [34.39574750992602]
  We provide a principled characterization of the properties that a fair influence algorithm should satisfy. Under this framework, the trade-off between fairness and efficiency can be controlled by a single design aversion parameter. Our framework encompasses as special cases leximin and proportional fairness.
  arXiv  Detail & Related papers  (2020-06-14T14:08:10Z)
• The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies [119.07163415116686]
  We train social planners that discover tax policies that can effectively trade-off economic equality and productivity. We present an economic simulation environment that features competitive pressures and market dynamics. We show that AI-driven tax policies improve the trade-off between equality and productivity by 16% over baseline policies.
  arXiv  Detail & Related papers  (2020-04-28T06:57:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.