As data scientists grapple with increasingly complex ethical decisions in machine learning (ML) and data science, the field of algorithmic fairness has offered multiple solutions, from formal mathematical definitions to holistic notions of fairness drawn from various academic disciplines. However, navigating and implementing these fairness approaches in practice remains an ongoing challenge. In this paper, we draw a parallel between the types of problems arising in algorithmic fairness and urban planning. We frame algorithmic fairness problems as `wicked problems,' a term originating from the planning and policy space to describe the intractable, value-laden, and complex nature of this work. As such, we argue that the field of algorithmic fairness can learn from theoretical work in urban planning in ameliorating its own set of wicked problems. Urban planning is typically concerned with practical issues of governance, resource allocation, stakeholder engagement, and conflicts involving deep-seated differences. These are challenges that existing fairness frameworks can easily overlook. We present a flexible framework for designing fairer algorithms based on the urban planning theory approach of critical pragmatism -- a reflective and deliberative approach to addressing wicked problems that considers what practitioners actually do in the face of conflict and power. We provide specific recommendations and apply them to several case studies in ML and algorithm design: automated mortgage lending, school choice, and feminicide counterdata collection. Researchers and practitioners can incorporate these recommendations derived from urban planning into their ongoing work to more holistically address practical problems arising in fair algorithm design.

翻译：随着数据科学家在机器学习（ML）和数据科学领域面临日益复杂的伦理决策，算法公平性领域已提供了多种解决方案，从形式化的数学定义到源自不同学科的整体性公平概念。然而，如何在实践中引导和实施这些公平性方法仍是一项持续挑战。本文类比了算法公平性问题与城市规划问题之间的类型相似性。我们将算法公平性问题定义为"棘手问题"——这一术语源于规划与政策领域，用以描述此类工作难以解决、充满价值判断且高度复杂的本质。由此，我们认为算法公平性领域可借鉴城市规划的理论成果来缓解其自身的棘手问题。城市规划通常关注治理、资源分配、利益相关者参与以及涉及深层分歧的冲突等实践问题，而这些正是现有公平性框架容易忽视的挑战。我们基于城市规划理论中的批判实用主义方法（一种应对棘手问题的反思性与协商性方法，考量实践者在面对冲突与权力时的实际行为），提出了一个设计更公平算法的灵活框架。我们提供了具体建议，并将其应用于机器学习与算法设计的多个案例研究：自动抵押贷款审批、学校选择及女性杀戮事件反数据收集。研究人员与实践者可将这些源自城市规划的建议融入日常工作中，从而更全面地解决公平算法设计中产生的实际问题。