Stakeholder Engagement
The process of involving affected parties (e.g., users, regulators, impacted communities) in AI development and oversight to ensure diverse perspectives and buy-in.
Definition
A deliberate practice of consulting and collaborating with all parties who have a stake in an AI system—end users, domain experts, civil-society groups, and regulators—through workshops, surveys, focus groups, and advisory panels. Engagement ensures that diverse values, concerns, and contextual insights shape requirements, design, and governance policies, leading to more ethically robust and socially accepted AI deployments.
Real-World Example
Before deploying a predictive-policing AI, a city held town-hall meetings with community leaders, civil-rights advocates, and law-enforcement to gather input on acceptable use cases, transparency requirements, and appeal processes. Their feedback led to tightening data-sources and adding a civilian oversight board for ongoing review.
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