The key idea: abstract ethical principles in IBM’s applied training workshops are put into practice when participants actively apply those principles to specific, realistic cases (not just hear about them in theory).

In typical IBM-style ethics or “applied training” sessions, this looks like:

  • Working in groups on detailed, real‑world AI or data use cases, then deciding how to handle issues like bias, privacy, transparency, and accountability using IBM’s “ethics by design” methods.
  • Using IBM’s concrete tools and playbooks (fairness toolkits, governance templates, ethics checklists) to walk through the entire lifecycle of a system and identify risks and mitigations.
  • Role‑playing stakeholder discussions (clients, end users, regulators, internal AI Ethics Board) and making go/no‑go or redesign decisions that reflect IBM’s AI principles and pillars of trust in practice.

So, if you’re choosing among options on a test, the best illustration is the one where learners are given specific scenarios or use cases and asked to apply IBM’s ethical principles and toolkits to make concrete design or deployment decisions, rather than just listening to a lecture or reviewing generic examples.