Co-Producing AI: Toward an Augmented, Participatory Lifecycle
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arXiv
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| Format: | Preprint |
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2025
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| author | Mushkani, Rashid Berard, Hugo Ammar, Toumadher Chatonnier, Cassandre Koseki, Shin |
| author_facet | Mushkani, Rashid Berard, Hugo Ammar, Toumadher Chatonnier, Cassandre Koseki, Shin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00138 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Co-Producing AI: Toward an Augmented, Participatory Lifecycle Mushkani, Rashid Berard, Hugo Ammar, Toumadher Chatonnier, Cassandre Koseki, Shin Artificial Intelligence 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. |
| title | Co-Producing AI: Toward an Augmented, Participatory Lifecycle |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.00138 |