AI Literacy for Real Decision Making
Single Track
8 tutorials · ~3 hours total
Spot AI failure modes before they become incidents and make AI deployment decisions that hold up under scrutiny.
How AI Fails and How to Respond
Learn the six AI failure modes that cause real organizational harm, then map each one to the right response protocol.
Model Limitations and What They Mean for You
Understand the fixed limitations of AI models so you can design around them instead of discovering them in production.
Privacy Risks in AI Systems
Map the privacy risks created by AI systems: prompt logging, data residency, memorization, output leakage, and erasure obligations.
Bias Risk: What It Is and How to Catch It
Understand AI bias as a measurable system behavior, then learn counterfactual testing, disaggregated evaluation, and response protocols.
Prompt Injection: The Attack You're Not Testing For
Learn direct, indirect, and stored prompt injection attack surfaces, then apply layered defenses for tool-enabled AI systems.
AI Literacy Expectations in 2026
Understand what AI literacy means by role in 2026, including EU AI Act Article 4 expectations and practical evidence of training.
Serious Training Reduces Harm
Design an AI literacy program that changes behavior: role-specific content, scenario assessment, incident learning, and measurable outcomes.
Decision Framework: When to Use AI and When Not To
Use a practical decision matrix and five-question checklist to decide when AI is appropriate, conditional, experimental, or too risky.