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.

翻译：系统思维为我们提供了一种建模算法公平性问题的方法，使我们能够将关于数据生成过程中可能存在的偏见的先验知识和假设进行编码。我们可以将这些认知编码为一系列因果图，从而将AI/ML系统与政治和法律联系起来。这使得我们能够结合机器学习、因果推断和系统动力学等技术，以捕捉公平性问题的不同涌现特征。借助系统思维，我们可以帮助政治光谱两端的政策制定者理解不同类型公平政策所涉及的复杂权衡，为设计符合其政治议程和社会共同民主价值观的AI政策奠定社会技术基础。