Co-Producing AI: Toward an Augmented, Participatory Lifecycle

TL;DR

This paper proposes an augmented AI lifecycle emphasizing co-production, diversity, and participatory governance to address biases and ethical challenges in AI development.

Contribution

It introduces a new five-phase AI lifecycle centered on co-production and multidisciplinary collaboration, grounded in empirical and theoretical frameworks.

Findings

The lifecycle promotes distributed authority and iterative knowledge exchange.

Workshops informed the lifecycle design.

Relates to existing ethical frameworks.

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.