# Co-Producing AI: Toward an Augmented, Participatory Lifecycle

arXiv: https://arxiv.org/abs/2508.00138 · HTML: https://mlsanity.com/paper/2508.00138/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 97 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 20 of 20)

- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [On the Apparent Conflict Between Individual and Group Fairness](https://mlsanity.com/paper/1912.06883/) (2019)
- [Collective Constitutional AI: Aligning a Language Model with Public Input](https://mlsanity.com/paper/2406.07814/) (2024)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence](https://mlsanity.com/paper/2007.04068/) (2020)
- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech](https://mlsanity.com/paper/2010.02716/) (2020)
- [Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics](https://mlsanity.com/paper/2202.08792/) (2022)
- [Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach](https://mlsanity.com/paper/2003.07703/) (2020)
- [An Artificial Intelligence Life Cycle: From Conception to Production](https://mlsanity.com/paper/2108.13861/) (2021)
- [LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces](https://mlsanity.com/paper/2503.01894/) (2025)
- [Towards a Privacy and Security-Aware Framework for Ethical AI: Guiding the Development and Assessment of AI Systems](https://mlsanity.com/paper/2403.08624/) (2024)
- [Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies](https://mlsanity.com/paper/2503.12613/) (2025)
- [From Efficiency to Equity: Measuring Fairness in Preference Learning](https://mlsanity.com/paper/2410.18841/) (2024)

## Built on by (most cited first, 2 of 2)

- [Street Review: A Participatory AI-Based Framework for Assessing Streetscape Inclusivity](https://mlsanity.com/paper/2508.11708/) (2025)
- [Benchmarks for Vision-Language Models in Urban Perception Should Be Reliability-Aware and Negotiated](https://mlsanity.com/paper/2606.00871/) (2026)

## Abstract (arXiv)

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.