What governance artifacts are essential for an AI project?

What governance artifacts are essential for an AI project?

• A. Model inventory, decision logs, data lineage, risk register, ethics and bias assessment, release governance, audit trails, deployment playbooks
• B. Source code repository and change log only
• C. User manuals and release notes only
• D. Marketing materials and stakeholder emails

Answer: (B)

In AI project governance, you need visibility and control that span data, models, and how they are deployed. Relying only on a source code repository and a change log covers code history, but it misses critical aspects that influence safety, accountability, and compliance. Without evidence of data provenance and model behavior, you can’t reliably audit decisions or reproduce results. A solid governance artifact set includes components that track and govern the whole lifecycle: a model inventory or registry to know what models exist and their versions; data lineage to show where data came from and how it’s transformed; a risk register to document potential harms and mitigation steps; an ethics and bias assessment to evaluate fairness and societal impact; release governance to control when and how models are moved to production; and audit trails that log who did what and when. Deployment playbooks help standardize rollout, monitoring, and rollback, while ongoing logs and monitoring results close the loop by showing real-world performance and compliance status. With this broader suite, you gain traceability, accountability, and the ability to demonstrate responsible AI practices. That’s why, in practice, governance hinges on more than just code history, and a comprehensive set of governance artifacts is considered essential.

In AI project governance, you need visibility and control that span data, models, and how they are deployed. Relying only on a source code repository and a change log covers code history, but it misses critical aspects that influence safety, accountability, and compliance. Without evidence of data provenance and model behavior, you can’t reliably audit decisions or reproduce results.

A solid governance artifact set includes components that track and govern the whole lifecycle: a model inventory or registry to know what models exist and their versions; data lineage to show where data came from and how it’s transformed; a risk register to document potential harms and mitigation steps; an ethics and bias assessment to evaluate fairness and societal impact; release governance to control when and how models are moved to production; and audit trails that log who did what and when. Deployment playbooks help standardize rollout, monitoring, and rollback, while ongoing logs and monitoring results close the loop by showing real-world performance and compliance status.

With this broader suite, you gain traceability, accountability, and the ability to demonstrate responsible AI practices. That’s why, in practice, governance hinges on more than just code history, and a comprehensive set of governance artifacts is considered essential.