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Co-Producing AI: Toward an Augmented, Participatory Lifecycle

Rashid Mushkani, Hugo Berard, Toumadher Ammar, Cassandre Chatonnier, Shin Koseki

TL;DR

AI systems disproportionately affect culturally marginalized groups, and existing guidelines fail to embed participatory governance across development. The authors propose an augmented AI lifecycle with five co-production phases—co-framing, co-design, co-implementation, co-deployment, and co-maintenance—grounded in design justice and expansive learning to redistribute decision-making authority toward affected publics. A scoping review and four multidisciplinary workshops ground the model, yielding mechanisms for iterative knowledge exchange, layered contextual privacy, and resource-aware engagement. While conceptual at present, the framework offers a scalable governance approach to produce contextually relevant and more just AI systems across domains such as health, finance, and public administration.

Abstract

Despite efforts to mitigate the inherent risks and biases of artificial intelligence (AI) algorithms, these algorithms can disproportionately impact culturally marginalized groups. A range of approaches has been proposed to address or reduce these risks, including the development of ethical guidelines and principles for responsible AI, as well as technical solutions that promote algorithmic fairness. Drawing on design justice, expansive learning theory, and recent empirical work on participatory AI, we argue that mitigating these harms requires a fundamental re-architecture of the AI production pipeline. This re-design should center co-production, diversity, equity, inclusion (DEI), and multidisciplinary collaboration. We introduce an augmented AI lifecycle consisting of five interconnected phases: co-framing, co-design, co-implementation, co-deployment, and co-maintenance. The lifecycle is informed by four multidisciplinary workshops and grounded in themes of distributed authority and iterative knowledge exchange. Finally, we relate the proposed lifecycle to several leading ethical frameworks and outline key research questions that remain for scaling participatory governance.

Co-Producing AI: Toward an Augmented, Participatory Lifecycle

TL;DR

AI systems disproportionately affect culturally marginalized groups, and existing guidelines fail to embed participatory governance across development. The authors propose an augmented AI lifecycle with five co-production phases—co-framing, co-design, co-implementation, co-deployment, and co-maintenance—grounded in design justice and expansive learning to redistribute decision-making authority toward affected publics. A scoping review and four multidisciplinary workshops ground the model, yielding mechanisms for iterative knowledge exchange, layered contextual privacy, and resource-aware engagement. While conceptual at present, the framework offers a scalable governance approach to produce contextually relevant and more just AI systems across domains such as health, finance, and public administration.

Abstract

Despite efforts to mitigate the inherent risks and biases of artificial intelligence (AI) algorithms, these algorithms can disproportionately impact culturally marginalized groups. A range of approaches has been proposed to address or reduce these risks, including the development of ethical guidelines and principles for responsible AI, as well as technical solutions that promote algorithmic fairness. Drawing on design justice, expansive learning theory, and recent empirical work on participatory AI, we argue that mitigating these harms requires a fundamental re-architecture of the AI production pipeline. This re-design should center co-production, diversity, equity, inclusion (DEI), and multidisciplinary collaboration. We introduce an augmented AI lifecycle consisting of five interconnected phases: co-framing, co-design, co-implementation, co-deployment, and co-maintenance. The lifecycle is informed by four multidisciplinary workshops and grounded in themes of distributed authority and iterative knowledge exchange. Finally, we relate the proposed lifecycle to several leading ethical frameworks and outline key research questions that remain for scaling participatory governance.

Paper Structure

This paper contains 33 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related literature
  3. Lifecycle models across domains
  4. Participation in the AI lifecycle
  5. Diversity, equity, and inclusion within AI practice
  6. Rationale for an augmented AI lifecycle
  7. Materials and Methods
  8. Literature Review
  9. Workshop Design
  10. Data Analysis
  11. Theoretical Orientation
  12. Synthesis Procedure
  13. Results
  14. Distributed authority.
  15. Iterative knowledge exchange.
  16. ...and 18 more sections

Figures (3)

  • Figure 1: The augmented AI lifecycle. Five co-production phases are linked by continuous knowledge exchange and shared accountability.
  • Figure 2: Phase-specific risks in a conventional AI lifecycle and corresponding mitigation via the co-design phase.
  • Figure 3: Arnstein’s ladder of citizen participation. A visual framework showing eight levels of citizen involvement, from nonparticipation to full citizen control in decision-making.