A Critical Pragmatism Approach for AI Ethics: Lessons from Urban Planning Theory

Abstract

As data scientists grapple with increasingly complex ethical decisions in machine learning (ML) and algorithm design, scholarship on AI ethics has offered multiple solutions, from formal mathematical definitions of fairness to holistic fairness frameworks drawn from various academic disciplines. Still, developing ethical and fair algorithmic systems in practice remains an ongoing challenge. In this paper, we draw a parallel between problems 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 AI ethics 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 purely technical approaches in AI easily overlook. We present a flexible set of recommendations 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 apply our recommendations to several case studies: 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 problems related to AI ethics.