Co-Producing AI: Toward an Augmented, Participatory Lifecycle

2025-07-31

AI Summary

Inherent biases in AI algorithms pose systemic risks to culturally marginalized communities. Method: This paper proposes a participatory AI lifecycle model that embeds diversity, equity, and inclusion (DEI) throughout governance—moving beyond conventional technical fixes. Grounded in design justice and expansive learning theory, it introduces a five-phase framework—*co-constructing*, *co-designing*, *co-creating*, *co-using*, and *co-maintaining*—centered on distributed power and iterative knowledge exchange. The model integrates participatory design, interdisciplinary collaboration, algorithmic fairness techniques, and ethics alignment to enable cross-domain collaborative decision-making and collective knowledge production. Contribution/Results: The work yields a structured governance pathway compatible with mainstream AI ethics frameworks, identifies key research challenges for scaling participatory governance, and delivers an actionable process-reengineering blueprint for developing fair and trustworthy AI systems.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Addressing AI risks and biases affecting marginalized groups
• Re-architecting AI production for diversity and inclusion
• Proposing a participatory AI lifecycle with co-production

Innovation

• Co-production in AI lifecycle redesign
• Multidisciplinary workshops inform development
• Iterative knowledge exchange enhances fairness