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

2026-05-04

AI Summary

This study addresses the limitations of prevailing algorithmic fairness approaches, which often rely on formalized or static ethical frameworks ill-suited to navigating real-world “wicked problems” such as governance challenges, resource allocation dilemmas, and conflicting stakeholder interests. To bridge this gap, the work innovatively draws on critical pragmatism from urban planning to propose a novel algorithm design framework that foregrounds practitioners’ situated agency—emphasizing negotiation, reflection, and action within contexts of power asymmetry and conflict. Through empirical case studies in automated mortgage approval, school district assignment, and gender-based violence data collection, the research formulates actionable design recommendations. These offer machine learning systems a viable pathway to reconcile ethical complexity with practical feasibility, demonstrating across multiple domains the framework’s effectiveness in enhancing algorithmic fairness.

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.

Problem

• algorithmic fairness
• wicked problems
• urban planning
• stakeholder engagement
• value-laden conflicts

Innovation

• algorithmic fairness
• critical pragmatism
• wicked problems
• urban planning theory
• stakeholder engagement