Skip to main content

Regulatory compliance frameworks and operational policies

Instruction and application
Complete

Don't let your model get shut down.​

What if you built an incredible machine learning model—only to have it shut down because you missed a critical compliance rule or overlooked a hidden risk?

ML systems operate within a complex web of legal, ethical, and industry-specific expectations. To build trustworthy systems, you need to understand how compliance frameworks shape your design, deployment, and operational decisions.

Microscope illustration

Legislation and standards shaping ML deployment​

Global frameworks provide essential guidance for building safe, fair, and compliant ML systems.

  • EU AI Act: Categorises AI into risk levels. High-risk systems (e.g., recruitment tools) require human oversight, detailed documentation, and transparency.
  • ISO/IEC 23053:2022: Provides a lifecycle management framework, emphasising risk management for challenges like model drift and adversarial attacks.
  • NIST AI Risk Management Framework (AI RMF): Guides organizations in mapping, measuring, and managing AI risks systematically.

Compliance Structures​

Organisations translate these standards into internal policies:

  • Data access controls (RBAC): Limiting who can modify or deploy models.
  • Deployment reviews: Formal sign-off covering bias tests and legal approval.
  • Ethics committees: Cross-functional boards reviewing high-risk systems.

From policy to practice​

Having governance policies on paper isn’t enough. ML teams need to translate these rules into concrete, day-to-day practices:

  • Model explainability tools: Use methods like SHAP or LIME to clarify predictions for stakeholders.
  • Human oversight checkpoints: Embed regular review points where humans validate AI outputs.
  • Version control (DVC/MLflow): Track changes to datasets and configurations to ensure reproducibility.
  • Audit-friendly logging: Capture data access, model predictions, and confidence scores for a clear trail.

Scenario: Insurance Fraud Detection​

A team develops a model to detect fraud. Before deployment, they perform a bias audit to ensure no demographic is unfairly flagged, acompliance review for anti-discrimination laws, and prepare amodel documentation package for regulators.

Action item: Put principles into practice​

Your team is developing a model to predict hospital bed availability. As you near deployment, you must ensure the model is transparent, accountable, and audit-ready.

Reflection: Hospital Bed Model
1. How would you ensure the model's predictions are transparent to medical staff?

Type your response here...

2. What specific logging would you implement to satisfy a compliance audit?

Type your response here...