Building Trustworthy AI Systems.
If you're building anything with AI — apps, workflows, products — these principles are non-negotiable.
After this lesson you'll know
- The 6 principles of trustworthy AI systems
- How to build human oversight into AI workflows
- Red flags in AI products and services
- How to evaluate whether an AI tool is safe to use
The Principles
6 principles of trustworthy AI.
1
Human Oversight
Humans can always intervene, correct, or override AI decisions. There's always a way to appeal an AI-made decision.
2
Explainability
You can understand WHY the AI made a decision. "The algorithm decided" isn't an explanation — it's a cop-out.
3
Fairness
The system is tested for bias across different groups. Disparate impacts are measured, reported, and mitigated.
4
Privacy by Design
Data protection isn't an afterthought — it's built into the system from day one. Minimum data collection. Clear consent.
5
Robustness
The system handles edge cases, adversarial inputs, and failures gracefully. It doesn't break in dangerous ways.
6
Accountability
Someone is responsible. If the AI causes harm, there's a person or team who owns the outcome — not "the algorithm."
Human-in-the-Loop
Building human oversight into AI workflows.
Even if you're just building a simple AI workflow — like using AI to draft emails that get sent automatically — think about where humans need to be in the loop:
Low Stakes (automate freely)
- Internal notifications
- Data formatting
- Content tagging/categorization
- Draft generation (with human review before send)
High Stakes (human must approve)
- Customer communications
- Hiring/screening decisions
- Financial transactions
- Anything published publicly
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