Training by theme
Quality and ethics
Output verification, bias, intellectual property, transparency: the course that keeps the human accountable for the quality and fairness of what AI produces.
Why this training
AI's apparent objectivity is a trap.
A cited source may be fabricated, a training bias passes silently, and a creation from a simple prompt is generally not protectable. This course installs "zero trust" toward deliverables, bias detection and the right transparency reflexes.
The modules in this course
Verifying an AI's answer
C04M01
Adopt "zero trust" toward AI deliverables.
Apply methodical reflexes to check the accuracy of AI-generated information.
RegisterBias and fairness: origins and detection
C04M02
Understand where bias comes from and see through apparent objectivity.
Understand where algorithmic bias in training data comes from and learn to detect it.
RegisterIntellectual property and generated content
C04M03
Grasp the copyright grey zone and the risk of infringement.
Determine the legal status of AI-generated works and prevent infringement risks.
RegisterTransparency
C04M04
Know when disclosure is required and how to phrase it.
Set the rules for disclosing AI use and include the appropriate transparency notices.
RegisterReady to equip your team?
Enroll your teams in our AI-literacy and governance training, from the common core to function-specific use cases.
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