用系统思维建模算法公平性，连接技术与政策
A Systems Thinking Approach to Algorithmic Fairness
- 将偏见假设转化为因果图，整合机器学习与因果推断
- 揭示不同公平政策背后的复杂权衡关系
- 为政治立场不同的决策者提供可对齐价值观的政策设计基础
系统思维为算法公平性问题提供了建模方法，使我们能够编码关于数据生成过程中潜在偏见的先验知识与假设。通过构建一系列因果图，可将人工智能/机器学习系统与政治及法律体系关联起来。该方法融合机器学习、因果推断与系统动力学技术，捕捉公平问题的多重涌现特征。利用系统思维，可帮助政治立场各异的政策制定者理解不同公平政策带来的复杂权衡，为设计契合其政治议程与社会共同民主价值的AI政策提供社会技术基础。
原文摘要 · Abstract (English)
Systems thinking provides us with a way to model the algorithmic fairness problem by allowing us to encode prior knowledge and assumptions about where we believe bias might exist in the data generating process. We can then encode these beliefs as a series of causal graphs, enabling us to link AI/ML systems to politics and the law. This allows us to combine techniques from machine learning, causal inference, and system dynamics in order to capture different emergent aspects of the fairness problem. We can use systems thinking to help policymakers on both sides of the political aisle to understand the complex trade-offs that exist from different types of fairness policies, providing a sociotechnical foundation for designing AI policy that is aligned to their political agendas and with society's shared democratic values.
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