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，它允许我们对数据生成过程中可能存在偏见的先验知识和假设进行编码。随后，我们可以将这些信念编码为一系列因果图，从而将人工智能/机器学习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中的技术，以捕捉公平性问题中涌现的不同方面。我们可以利用系统思维帮助政治两派的政策制定者理解不同类型公平政策之间存在的复杂权衡，从而为设计与其政治议程及社会共享民主价值观相一致的AI政策提供社会技术基础。