Multi-Stakeholder Engagement
Involving diverse groups (e.g., legal, ethics, operations, end users) in AI governance processes to ensure balanced risk oversight and alignment with business goals.
Definition
A governance best practice where cross-functional teams—legal, compliance, data science, domain experts, and affected user representatives—participate in impact assessments, policy development, and review boards. This ensures that diverse perspectives shape AI decisions, align models with organizational values, and anticipate unintended consequences. Structured engagement includes workshops, surveys, and decision-logging for transparency.
Real-World Example
Before deploying an AI-driven hiring tool, HR, legal, ethics, and candidate-representative groups collaboratively review the model’s objectives, training data, and assessment criteria. Their joint feedback leads to adjustments that better protect candidate fairness and reduce legal exposure.
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