Skills application

Instruction and application

Complete

Compliance and risk management in ML

In this skills application, you will apply what you’ve learned about compliance and risk management in machine learning to a real-world scenario. You’ll demonstrate how to align a system with regulatory requirements, design risk mitigation strategies, assign accountability, and ensure audit readiness.

Success criteria

To successfully complete this skills application, you must:

• Identify relevant external regulations and internal policies.
• Map system-level risks and assign ownership.
• Propose mitigation strategies and documentation methods.
• Recommend practices for audit readiness and escalation.

Context

You are working with a logistics company deploying an ML model to optimise delivery routing. The system integrates geolocation data and third-party traffic APIs. The company must meet both internal safety standards and external regulatory requirements for data use, model transparency, and system oversight.

Your task is to design a comprehensive compliance and risk oversight plan for this system.

Instructions

Follow the prompts in the form below to complete your analysis. Completing this activity will “unlock” the solution example on the following page.

Exercise: Logistics Compliance Plan

1. Compliance and regulatory alignment: Identify two external regulations and one internal policy. Explain how they shape data processing and transparency.

Type your response here...

2. Risk assessment and ownership map: List four system-level risks and assign an owner for each (e.g., platform lead, model owner).

Type your response here...

3. Mitigation integration: Propose three mitigation actions (e.g., workflow gating) and explain how they will be logged to support audit readiness.

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4. Audit readiness and escalation: Recommend two practices for ongoing readiness and design a simple escalation plan (levels, actions, roles).

Type your response here...