Designing and mantaining AI/ML models that help data subjects, are explainable, are not biased, and are compliant.
InstructorVasco PatrícioThe structure provides specialized paths for understanding how to explain discriminative AI through attribution and generative AI through faithfulness and evidence.
The curriculum includes various 'Lab' items designed to simulate real-world tasks like fairness control and hallucination reduction.
Editorial course preview
This view highlights a concrete, legible example from the course presentation.
The presenter introduces Module 4 on ethical risks, using hand gestures to emphasize key points while seated in a well-lit room with decorative artwork.
Instructional signals suggest a tendency to read directly from slide decks, which may impact engagement and clarity for some learners. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
The curriculum contains several modules explicitly marked as older Version 1.0 content.
The curriculum aligns with professional roles, though some signals suggest the scope may not be perfectly calibrated for all levels.
