AI Safety and Ethics: Hallucinations, Bias, and Guardrails
AI is impressive, but it is not infallible. It hallucinates facts, amplifies biases, and can be manipulated through prompt injection. If you are building with AI — or even just relying on it at work — you need to understand these failure modes. This path covers the full safety landscape: why hallucinations happen, how prompt injection attacks work, what guardrails and safety layers look like in practice, and the ethical questions every AI practitioner should be asking.
What You'll Learn
- 1Why AI hallucinates and practical techniques to detect and reduce confabulation
- 2Prompt injection: how attackers manipulate AI systems and how to defend against it
- 3Guardrails and safety layers that prevent AI from going off the rails in production
- 4Bias and fairness: where AI bias comes from and what responsible mitigation looks like
- 5Evaluation frameworks for measuring whether your AI application is actually working
- 6When not to use AI — and the ethical boundaries every team should draw
Curated Lessons (8)
Free, interactive lessons you can complete on your phone in 5-10 minutes each.
Hallucinations — When AI Makes Stuff Up
When AI Gets It Wrong
Prompt Injection — When Users Hack Your AI
When AI Gets It Wrong
Guardrails & Safety Layers
When AI Gets It Wrong
Bias and Fairness
When AI Gets It Wrong
Evaluating AI Apps
When AI Gets It Wrong
When NOT to Use AI
When AI Gets It Wrong
AI Ethics Beyond Bias
When AI Gets It Wrong
When Agents Fail — And How to Keep Humans in Control
Agents — AI That Takes Action
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