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AI risks: spotting, measuring and responding to them

AI risks: spotting, measuring and responding to them

An AI that fails in plain sight causes little trouble: it gets corrected. The one that costs dearly is the one that fails in the same tone in which it succeeds, in front of someone who has stopped looking. This course brings together the risks that have emerged throughout the learning path and gives them a method: they are ranked by likelihood and by harm, and each one is assigned a proportionate control, neither the same for everything nor none at all.

Private Course
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Responsible Carlos Pascual
Last Update 2026-10-06
Completion Time 4 hours 45 minutes
Members 1
Basic Data, Analytics and AI
  • Block 1: Trusting too much: reliability risks
    3Lessons · 1 hr 10 min
    • Mistakes that look like hits
    • Attention that dozes off and the craft that is lost
    • Block 1 exam
      10 xp
  • Block 2: Data and security: what goes out and what comes in
    3Lessons · 1 hr 15 min
    • What you give a machine
    • What a machine lets in
    • Block 2 exam
      10 xp
  • Block 3: People affected: bias, decisions and transparency
    3Lessons · 1 hr 5 min
    • When the model decides about people
    • Saying it is a machine, and being able to appeal
    • Block 3 exam
      10 xp
  • Block 4: Answering for it: the regulation, the policy and the signature
    3Lessons · 1 hr 10 min
    • The European regulation, by risk level
    • Your team's usage policy
    • Block 4 exam
      10 xp