Position: The Right to AI

This position paper proposes a “Right to AI,” which asserts that individuals and communities should meaningfully participate in the development and governance of the AI systems that shape their lives. Motivated by the increasing deployment of AI in critical domains and inspired by Henri Lefebvre's concept of the “Right to the City,” we reconceptualize AI as a societal infrastructure, rather than merely a product of expert design. In this paper, we critically evaluate how generative agents, large-scale data extraction, and diverse cultural values bring new complexities to AI oversight. The paper proposes that grassroots participatory methodologies can mitigate biased outcomes and enhance social responsiveness. It asserts that data is socially produced and should be managed and owned collectively. Drawing on Sherry Arnstein’s Ladder of Citizen Participation and analyzing nine case studies, the paper develops a four-tier model for the Right to AI that situates the current paradigm and envisions an aspirational future. It proposes recommendations for inclusive data ownership, transparent design processes, and stakeholder-driven oversight. We also discuss market-led and state-centric alternatives and argue that participatory approaches offer a better balance between technical efficiency and democratic legitimacy.

Artificial intelligence (AI) systems increasingly influence decisions in everyday life. Yet most of the design and governance of these systems is handled by a small group of companies and institutions, with limited public involvement.This paper introduces the idea of a “Right to AI,” which suggests that people and communities should have opportunities to participate in decisions about how AI is developed, used, and regulated. Drawing from models in urban planning and participatory governance, we frame AI as a form of societal infrastructure—something that affects collective life and could be shaped through more inclusive processes.We propose a model that ranges from consumer-based approaches, where users are passive recipients of AI outcomes, to systems where citizens share real decision-making power. This model applies to both current practices and possible future directions.The paper examines case studies of participatory AI projects and outlines conditions that can support meaningful public involvement. While challenges remain, including questions of expertise, scale, and accountability, the goal is to identify practical steps that could help make AI governance more responsive to the communities it affects.