AI Training Module: Responsible AI Practices

1. Introduction

This training module provides employees with the knowledge and skills necessary to design, develop, deploy, and manage AI responsibly within the organization. It is based on the organization’s Responsible AI Policy and the NIST AI Risk Management Framework (RMF).

2. Learning Objectives

By the end of this training, participants will be able to:

1. Understand the organization’s AI, ML, and LLM policies.

2. Identify and assess AI risks during development and deployment.

3. Apply data governance and privacy controls to AI systems.

4. Implement transparency, monitoring, and auditing for ML and LLM features.

5. Recognize the importance of human oversight and responsible usage.

6. Respond appropriately to AI-related incidents.

3. Training Units

Unit 1: Understanding AI in Our Organization

• What is AI, ML, and LLM?

• Planned AI adoption in the next 12 months.

• Use cases within our solutions.

• Activity: Identify AI-enabled features in our current products.

Unit 2: AI Risk Management

• Introduction to AI risk models.

• NIST AI RMF principles.

• Mapping, measuring, and managing AI risks.

• Case Study: Review a past AI incident and evaluate how risks could have been mitigated.

Unit 3: Governance and Controls

• How AI features can be enabled/disabled.

• Incident response and timely disablement of AI functions.

• Logging and auditing requirements.

• Supply-chain risk planning.

• Quiz: Multiple-choice questions on governance and incident response.

Unit 4: Data Governance & Privacy

• Business rules for sensitive data protection.

• Data separation (training vs. operational).

• Vetted, validated, and verified training data.

• Removal of sensitive data upon request.

• Workshop: Role-play scenario where sensitive data is ingested and must be removed.

Unit 5: ML-Specific Controls

• Transparency and documentation of models.

• Authenticating and verifying feedback.

• Monitoring, auditing, and adversarial testing.

• Watermarking training data.

• Exercise: Review an ML output and determine how to validate it.

Unit 6: LLM-Specific Controls

• Limiting privileges by default.

• Human oversight for critical actions.

• Resource usage limitations.

• Plugin restrictions.

• Tuning and validation mechanisms.

• Scenario Analysis: Evaluate risks of allowing multiple LLM plugins in one request.

Unit 7: Human Oversight and Responsible Use

• Why human-in-the-loop is essential.

• Identifying decisions that must not be fully automated.

• Ethical AI considerations (bias, fairness, explainability).

• Discussion: When should humans override AI decisions?

Unit 8: Compliance and Enforcement

• Organizational accountability.

• Staff responsibilities.

• Consequences of non-compliance.

• Knowledge Check: Policy Q&A.

4. Assessments

• Module Quizzes: At the end of each unit.

• Final Assessment: 20-question test covering governance, data, ML, LLM, and oversight.

• Certification: Employees must score 80% or higher to pass.