Participatory engagement
Also written: Participatory AI, Stakeholder involvement
Getting people involved in creating or managing AI systems, especially for governance and training.
Participatory engagement means including people in the process of building and running AI. This can involve getting feedback from users, letting communities have a say in how AI is used, or even having the public help train AI models.
For example, a lab might ask people to review AI-generated text for bias or to suggest ways to make an AI assistant more helpful and safe. This helps make AI tools that are more fair, useful, and trusted by the people who use them.
Participatory engagement in AI refers to the deliberate inclusion of diverse stakeholders in the design, development, evaluation, and governance of AI systems. This approach aims to democratize AI development by incorporating a broader range of perspectives beyond those of the developers themselves.
Methods can include public consultations, community advisory boards, citizen juries, and participatory data labeling or model evaluation. The goal is to align AI systems with societal values, improve their fairness and robustness, and enhance public trust, though it can introduce complexities in development timelines and decision-making processes.
Frontier AI labs are increasingly exploring participatory engagement as a way to address public concerns about AI's societal impact and ensure models align with diverse human values. Recent initiatives focus on integrating public input directly into model training and governance, moving beyond simple feedback mechanisms.
- Democratic inputs to AI grant program: lessons learned and implementation plansOpenAI · OpenAI News ·
- OpenAI ChatGPT
- OpenAI has funded projects focused on democratic AI governance and is exploring ways to integrate public input into its model training processes.
- User feedback
- User feedback is typically reactive, collected after a product is
- Crowdsourcing
- Crowdsourcing often involves large-scale task completion, whereas
- Beta testing
- Beta testing is a form of user testing focused on bug identificati
The concept of participatory engagement originates from democratic theory and community organizing, emphasizing the importance of involving affected parties in decision-making processes. In the context of AI, it's being adapted to address the societal implications of advanced AI systems.
Try asking an AI assistant like ChatGPT to explain a complex topic from the perspective of a specific community or user group. Then, ask it to refine its explanation based on hypothetical feedback from that group, simulating a participatory refinement process.
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
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Questions people ask
- Why is participatory engagement important for AI?
- It helps ensure AI systems are fair, safe, and aligned with the values of the diverse people they will affect, building trust and preventing unintended negative consequences.
- How do companies implement participatory engagement?
- Companies can use methods like public forums, advisory boards, and direct input during the AI development and training phases to gather diverse perspectives.
First seen on Subvolts Jan 16, 2024. Updated Sep 15, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.