Module 16
AI Ethics & Governance
Build AI that is powerful, responsible, and trusted
Lessons
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16.1
AI Risk Taxonomy
Locked45 min
16.2
Bias, Fairness & Representation
Locked40 min
16.3
AI Regulation & Compliance
Locked50 min
16.4
AI Safety Engineering
Locked45 min
Expert Lens
Founder
Think like a founder: what user pain, distribution channel, pricing, and retention loop does this AI capability unlock?
Systems
Think like a first-principles systems builder: separate demos from durable products, and measure the bottleneck the model actually removes.
Infrastructure
Think like an AI infrastructure leader: account for data, latency, cost, reliability, evaluation, and deployment constraints before scaling.
Objectives
- Understand and mitigate real AI risks in production
- Implement responsible AI practices
- Navigate AI regulation and compliance
- Build governance frameworks for organizational AI
Capstone
Responsible AI Framework
Create a governance framework: risk taxonomy, mitigation, policies, compliance checklist, and audit trail.
An audit-ready AI governance document meeting global regulatory standards.
Real Examples
- A solo operator replaces repetitive research, drafting, and QA loops with an AI-assisted workflow while preserving human review.
- A startup validates an AI feature by measuring time saved, accuracy, user retention, and willingness to pay before building a platform.
- An enterprise team moves from chatbot demo to governed internal assistant with retrieval, permissions, observability, and escalation.
Mastery Checks
- Design a privacy and access-control plan for user data.
- Write a red-team checklist for prompt injection, data leakage, and unsafe automation.
- Define incident response ownership for AI failures.