Compliance Review Support

Why this matters

Applied Scientists often ship models into products that handle user data, impact decisions, and require audits. Compliance Review Support helps you turn your ML work into traceable, review-ready evidence that satisfies privacy, safety, fairness, and transparency expectations. This speeds up approvals, reduces rework, and protects users and your organization.

• Product reality: regulators and customers ask for proof, not promises. You provide the proof.
• Common tasks: mapping requirements to artifacts, preparing model cards, running risk assessments, coordinating sign-offs, and setting up monitoring and incident response.
• Note: This is practical guidance to support compliance efforts, not legal advice.

Concept explained simply

Compliance Review Support means: identify which rules apply, build or collect the right evidence, fix gaps, and keep it current.

Mental model

• Layer 1 — Requirements: privacy (e.g., consent, data minimization), safety, fairness, transparency, security, and record-keeping.
• Layer 2 — Evidence: documents and measurements that prove what you did (data inventory, DPIA/PIA, model card, eval results, logs).
• Layer 3 — Controls: practices that keep the system compliant (access control, retention policies, monitoring, incident response).

Core workflow you can follow

1. Scope & classify
   Describe the AI system, intended use, affected users, data types, and business context. Classify risk level and identify applicable requirements.
2. Plan evidence
   Map each requirement to concrete artifacts (what document/test), an owner, and a due date.
3. Create & collect
   Produce missing artifacts: data map, DPIA/PIA, model card, evaluation reports, red-teaming results, and a monitoring plan.
4. Gap mitigation & sign-offs
   Address gaps (e.g., remove unnecessary data, add monitoring). Route for sign-offs (e.g., privacy, security, product, ethics board).
5. Launch & maintain
   Control changes, log decisions, monitor for drift and harms, and refresh documentation on updates.

Worked examples (3)

Example 1 — Text classifier using user feedback

• Context: You train a support-ticket classifier on user-submitted text.
• Requirements touched: data minimization, retention, user privacy, transparency.
• Evidence plan: data inventory, consent basis description, PII handling notes, retention policy, model card with performance/limitations.

Example 2 — Recommendation model for an e-commerce site

• Context: Personalized recommendations influence what users see.
• Requirements: fairness/anti-discrimination, transparency, explainability, monitoring.
• Evidence plan: fairness eval across key segments, top-k accuracy and coverage, explanation summary for end-users, bias mitigation notes, monitoring dashboard plan.

Example 3 — LLM assistant for internal productivity

• Context: An internal GPT-style assistant summarizes documents.
• Requirements: access control, confidentiality, safety, audit logs, red-teaming.
• Evidence plan: data access matrix, content filtering tests, red-team notes, prompt/version logs, incident response playbook.

Artifacts you’ll help produce

• System description and intended use statement
• Data map/inventory and lineage notes
• Privacy assessment (e.g., DPIA/PIA) with risks and mitigations
• Security controls summary (access, retention, encryption, audit logs)
• Model card or factsheet (metrics, datasets, limitations, update plan)
• Evaluation reports (fairness, robustness, safety/red-teaming)
• Monitoring plan (KPIs, thresholds, dashboards, alerting)
• Change log and decision log with sign-offs
• Incident response outline (how to pause, roll back, notify)

Hands-on exercise (mirrors the exercise below)

Goal: Create a one-page compliance evidence plan for a model you’re building or a fictional one.

1. Briefly describe the system, data, users, and intended use.
2. List 6–8 requirements relevant to your case (privacy, fairness, safety, transparency, security, monitoring).
3. For each requirement, map: Artifact, Owner, Status, Due date.
4. Identify top 3 gaps and write concrete mitigations.
5. Draft a minimal monitoring KPI set and alert thresholds.

• [ ] System scope is clear
• [ ] Every requirement maps to at least one artifact
• [ ] Each artifact has an owner and due date
• [ ] Gaps have named mitigations
• [ ] Monitoring includes fairness and performance where applicable

System: <name> — <purpose> — Users: <roles> — Data: <types>
Requirements → Evidence Plan
1) Privacy: data minimization → Data inventory + PII handling SOP (Owner: A, Due: 2026-02-01, Status: Draft)
2) Transparency: model card → v1 model card (Owner: B, Due: 2026-02-05, Status: Not started)
3) Fairness: parity tests → Eval report across segments (Owner: C, Due: 2026-02-07, Status: In progress)
...
Top Gaps: G1 <desc> → Mitigation <action> by <date>
Monitoring KPIs: Accuracy>X, Segment parity within Y, Incident rate<Z; Alert: pager on breach

Common mistakes and self-check

• Mistake: Writing documents after the build is finished. Fix: Plan evidence at project start and update continuously.
• Mistake: Listing policies without proof. Fix: Include logs, screenshots, dataset descriptors, and versioned reports.
• Mistake: Ignoring segment-level results. Fix: Always evaluate key user segments and explain thresholds.
• Mistake: No change control. Fix: Version prompts/models; re-run key evals on material changes.
• Mistake: Monitoring only performance. Fix: Monitor fairness, safety, drift, and data quality too.

Practical projects

• Build a lightweight model card generator: fill a template and export a PDF with metrics and limitations.
• Create a fairness evaluation notebook that outputs a one-page report with segment metrics and traffic-light flags.
• Design a monitoring plan: define KPIs, thresholds, and example alerts for a chosen ML system.

Who this is for

• Applied Scientists building or maintaining models in products.
• ML engineers and data scientists who support audits and launch reviews.
• Team leads who need predictable, review-ready AI deliveries.

Prerequisites

• Basic ML lifecycle knowledge (data, training, evaluation, deployment).
• Familiarity with version control and experiment tracking.
• Understanding of metrics and model documentation.

Learning path

1. Foundation: learn the requirements-evidence-controls mental model.
2. Documentation: practice writing concise system descriptions and model cards.
3. Risk assessment: run a lightweight DPIA/PIA and log mitigations.
4. Evaluation: create fairness/safety evaluation checklists and thresholds.
5. Operations: define monitoring, alerts, change control, and incident response.

Next steps

• Pick one ongoing model and draft its evidence plan this week.
• Book a 30-minute review with privacy/security stakeholders to validate gaps.
• Automate one artifact (e.g., generate a model card from your training logs).

Mini challenge

In 20 minutes, write three risk statements for your model, each with at least one measurable KPI and an alert rule. Keep it to 10 lines total. Share with a teammate for critique.

Quick Test and progress

The quick test below is available to everyone. If you log in, your progress and results will be saved; otherwise, you can still take it without saving.