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The Right to AI

Rashid Mushkani, Hugo Berard, Allison Cohen, Shin Koeski

TL;DR

This work argues for a Right to AI, a collective governance framework that treats AI as societal infrastructure and aims to empower communities to shape data practices, objectives, and oversight. It grounds the proposal in Lefebvre's Right to the City and Arnstein's ladder, and develops a four-tier participation model, supported by nine case studies, to map pathways from consumer-level involvement to citizen-controlled governance. Key contributions include the four-tier ladder, participatory governance considerations, data trust concepts, and concrete recommendations for inclusive data ownership and auditing. The proposed approach seeks to balance technical efficiency with democratic legitimacy, offering a path toward more equitable, transparent, and accountable AI systems that reflect diverse values and local contexts.

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.

The Right to AI

TL;DR

This work argues for a Right to AI, a collective governance framework that treats AI as societal infrastructure and aims to empower communities to shape data practices, objectives, and oversight. It grounds the proposal in Lefebvre's Right to the City and Arnstein's ladder, and develops a four-tier participation model, supported by nine case studies, to map pathways from consumer-level involvement to citizen-controlled governance. Key contributions include the four-tier ladder, participatory governance considerations, data trust concepts, and concrete recommendations for inclusive data ownership and auditing. The proposed approach seeks to balance technical efficiency with democratic legitimacy, offering a path toward more equitable, transparent, and accountable AI systems that reflect diverse values and local contexts.

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.

Paper Structure

This paper contains 66 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Positioning the Right to AI
  4. The Right to the City
  5. Ladder of Citizen Participation
  6. Grassroots Engagement
  7. Arguments for AI as Societal Infrastructure
  8. Broad Societal Impact
  9. Essential Role in Daily Life
  10. Collective Management
  11. Arguments for the Right to AI
  12. Democratic Legitimacy
  13. Social Justice and Pluralism
  14. Epistemic Autonomy
  15. The Production of Data
  16. ...and 51 more sections

Figures (2)

  • Figure 1: The Ladder of Citizen Participation, illustrating levels of public involvement from manipulation to citizen control.
  • Figure 2: Progression in stakeholder power from minimal engagement (Consumer-Based) to robust self-governance (Communal Sovereignty). This categorization helps assess current initiatives and guide transitions toward more participatory models.