PEAs in PODs: Co-Production of Community Based Public Engagement for Data and AI Research
Abstract
The operationalization of ethical principles, current and emerging legalization, and the understanding and mitigation of potential consequences to individuals and society are key challenges in the design, development, and deployment of Artificial Intelligence (AI) driven systems. As part of forthcoming global legislation, organisations, businesses, and researchers developing AI solutions as a service or innovating novel applications will need to openly address ethical principles such as bias, fairness, explainability, transparency, data privacy, accountability, and safety through AI Governance. Developing responsible AI is essential to building public trust, yet few AI researchers know how to engage and capture the insight of diverse people, especially those with lived experiences. This paper presents, an operationalization model for Co-production and Public Engagement in the field AI which focuses on Voice, Value and Variety to guide how academics and businesses might approach meaningful public engagement in the context of research and development of AI products and services. The model is applied within a project called PEAs in PODs which seeks to empower the research and development community to engage meaningfully with traditionally marginalized communities on the subject of AI. Recommendations are made for the integration of co-production methods into AI research to enable meaningful public engagement for all AI researchers.
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AI technologies often further augment, rather than close, gaps connected to digital divides, conflicting cultural values, socioeconomic inequalities and long-term sustainable change, because they lack meaningful community engagement during development.
“As a result, AI technologies often further augment, rather than close the gaps between, issues connected to digital divides, conflicting cultural values, socioeconomic inequalities and long-term sustainable change, because they lack meaningful community engagement during development [14].”
From Designing Responsible AI with Indigenous Communities: Towards Participatory AI with ovaHimba and San People in Namibia (2026) · cites this paper as [14]
Cited by
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Indongo, N. N., Smith, R. C., Winschiers-Theophilus, H., Kandjengo, S., & Mbinge, U. (2026) · Proceedings of the 19th Participatory Design Conference 2026, Vol. 1: Full Papers · doi:10.1145/3796624.3796667
- Authors
- Crockett, K., Colyer, E., Coulman, L., Nunn, C., & Linn, S.
- Affiliations
- Manchester Metropolitan University (GB)
- Scientia Scripta,UK
- Noisy Cricket,UK
- Venue
- 2024 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2024-06-30
- Pages
- 1-10
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W4402351068
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
- e-space (Manchester Metropolitan University) (acceptedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Innovative Approaches in Technology and Social Development
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Keywords
- citizen participation, co-production, responsible AI, AI governance, marginalized communities, lived experience, AI ethics, data ethics, public trust in AI
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