AI pillar · Module 6 of 6

How to evaluate AI systems

Eventually you’ll need to decide whether to adopt an AI tool, approve a vendor, or assess an internal project. Here’s how to ask the right questions.

← Back to AI Fundamentals Training

The essential questions

  • What problem does this actually solve? Not “what does it do”—what business problem does it address? Is that problem worth solving?
  • How was it trained? What data? From where? Any obvious bias risks? Is the training data appropriate for your use case?
  • How accurate is it? What’s the error rate? On whose data? Does it work for your specific context and population?
  • What happens when it’s wrong? What’s the failure mode? How bad is a false positive vs. false negative? Who catches errors?
  • Who’s accountable? When something goes wrong, who owns it? The vendor? Your team? Is that clear in contracts?
  • What data does it see? What inputs does it need? Where does that data go? What about privacy and confidentiality?
  • Can you explain it? If a customer or regulator asks why a decision was made, can you answer? Do you need to?

Red flags to watch for

  • “It’s AI” as the only explanation
  • No documentation of training data
  • Accuracy claims without context
  • No plan for when it fails
  • Vendor can’t explain how it works
  • “Trust the algorithm” mentality
  • No human oversight in the loop
  • Vague privacy policies
  • No bias testing or monitoring
  • Unclear data retention and deletion

🧭 The bottom line

AI is a tool. Evaluate it like any other tool: Does it solve a real problem? What are the risks? What happens when it fails? If you can’t answer these questions, you’re not ready to deploy.

Free resources to go deeper

Nice work! What’s next?

You now have a solid foundation in AI—what it is, how it works, the risks, and how to evaluate it. Here’s where to go from here.

For hands-on learners

For leaders and decision-makers