A Critical Pragmatism Approach for Algorithmic Fairness: Lessons from Urban Planning Theory

• URL: http://arxiv.org/abs/2605.02773v1
• Date: Mon, 04 May 2026 16:17:49 GMT
• Title: A Critical Pragmatism Approach for Algorithmic Fairness: Lessons from Urban Planning Theory
• Abstract summary: We argue that the field of algorithmic fairness can learn from theoretical work in urban planning in ameliorating its own set of wicked problems.<n>We present a flexible framework for designing fairer algorithms based on the urban planning theory approach of critical pragmatism.
• Score: 4.40050181459927
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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