How can project teams ensure continuous ethical compliance throughout an AI project's lifecycle?

To ensure continuous ethical compliance, project teams must treat governance as an ongoing cycle rather than a one-time checklist:

1. Design Phase: Conduct an Algorithmic Impact Assessment and establish diverse governance teams to define ethical boundaries early.
2. Development Phase: Audit training data for bias and enforce Explainable AI (XAI) so model decisions can be audited.
3. Deployment Phase: Set up automated monitoring dashboards to track model drift and ensure the AI remains unbiased as it ingests new, real-world data.
4. Operations Phase: Create a clear "kill switch" protocol to take a model offline or roll it back if it breaches ethical thresholds.