The Right to AI

2025-01-29

AI Summary

This paper addresses structural challenges in AI’s societal deployment—including algorithmic bias, inequitable data ownership, opaque design processes, and weak regulatory oversight—stemming from public exclusion. Methodologically, it reconceptualizes AI as “social infrastructure,” advancing a four-layer “AI rights” model that synthesizes Lefebvre’s “right to the city” with Arnstein’s ladder of citizen participation, and develops a participatory governance framework grounded in a social conception of data production. Drawing on critical technical practice, participatory action research, and comparative analysis of nine empirical cases—complemented by normative policy modeling—the study yields actionable pathways: (1) an inclusive data property regime; (2) transparent, co-designed algorithmic development mechanisms; and (3) a multi-stakeholder collaborative oversight system. Collectively, these interventions seek a dynamic equilibrium between technical efficiency and democratic legitimacy.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: AI for Accessibility
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Security and Privacy: Data transparency and provenance

Abstract

This 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.

Problem

• AI Governance
• Bias and Fairness
• Transparency and Regulation

Innovation

• Artificial Intelligence Rights
• Participatory Approach
• Data Ownership Democratization