AI governance is the set of processes, documentation, and controls that ensure AI systems in an organization are accountable, auditable, and compliant. As the EU AI Act enters into force, governance is shifting from a good practice to a legal requirement for many AI applications. Building governance patterns from the start is significantly less expensive than retrofitting them.

Model Cards

A model card is a structured document that describes an AI model’s purpose, training data, performance characteristics, known limitations, and intended use boundaries. Originally a research practice, model cards are increasingly required by procurement teams, regulators, and enterprise security reviewers.

What a model card should cover:

  • Model purpose - What it is designed to do, what it is not
  • Training data - Source, date range, known gaps or biases in the training population
  • Performance metrics - Accuracy, precision, recall by relevant subgroup (not just overall)
  • Known limitations - Failure modes, out-of-distribution behavior, populations where accuracy degrades
  • Update history - When the model was last updated and why

For third-party models (e.g., Claude, Titan), the vendor provides model cards. For custom models, creating and maintaining a model card is the development team’s responsibility.

Decision Logging

For any AI system that influences consequential decisions - loan approvals, insurance risk scoring, content moderation, hiring screening - logging what the model decided and why is both operationally valuable and legally significant.

Minimum logging requirements: input data, model output and confidence score, decision rules applied (if any post-model decisioning), final decision, and timestamp. For high-stakes decisions, add: human reviewer identity and any override rationale.

Amazon CloudWatch and DynamoDB are standard choices for decision logging infrastructure. Retention periods should be set to match the longest applicable regulatory requirement (typically 5-7 years for financial decisions).

Bias Detection

AI models can produce systematically different outputs for different demographic groups, even when demographic data is not an explicit input. Regular bias testing checks whether model performance is consistent across age, gender, geography, language, and other relevant dimensions.

Implementation: create test sets stratified by relevant dimensions, run model predictions on each stratum, and compare performance metrics. Amazon SageMaker Clarify automates this analysis. Set thresholds for acceptable performance gaps; flag models that exceed them for review before deployment and during ongoing monitoring.

Approval Workflows

AI systems should not go to production without formal review. An approval workflow documents that the system has been evaluated for accuracy, bias, security, and operational readiness. Standard stages:

  1. Technical review - Does it work as specified?
  2. Bias and fairness review - Are performance gaps within acceptable bounds?
  3. Security review - Is data handled appropriately? Are model outputs sanitized?
  4. Legal/compliance review - Does it meet applicable regulatory requirements?
  5. Business sign-off - Has the process owner accepted the system?

Tools like Jira, ServiceNow, or dedicated MLOps platforms (SageMaker Studio) can automate the workflow tracking.

EU AI Act Considerations

The EU AI Act creates obligations based on risk classification. High-risk AI systems (including those used in credit scoring, employment, education, critical infrastructure, and healthcare) require: conformity assessment, technical documentation, accuracy and robustness testing, human oversight mechanisms, and registration in an EU database.

For systems that may qualify as high-risk, the governance infrastructure described above - model cards, decision logging, bias testing, approval workflows - aligns closely with what the Act requires. Building these practices now reduces the compliance effort when enforcement begins.

Prohibited AI practices under the Act (subliminal manipulation, social scoring, real-time public biometric surveillance with limited exceptions) should be reviewed against any planned AI use cases.