Training by theme
Quality and ethics
Output verification, bias, intellectual property, transparency: the path that keeps the human accountable for the quality and fairness of what AI produces.
Why this path
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 path installs "zero trust" toward deliverables, bias detection and the right transparency reflexes.
The modules in this path
Verifying an AI output: sources, figures, citations
M15
Adopt "zero trust" toward AI deliverables.
A source cited by AI may be fabricated: go back to the original document and recompute the figures. Verifying one output with another AI is risky.
RegisterBias and fairness: origins and detection
M16
Understand where bias comes from and see through apparent objectivity.
Bias comes from training data, which reflects human prejudice. Reduce it through diverse oversight and testing across different scenarios.
RegisterIntellectual property and generated content
M17
Grasp the copyright grey zone and the risk of infringement.
A creation from a simple prompt is generally not protectable, lacking human input. Legal value comes from creative contribution, not prompt length.
RegisterTransparency: when and how to disclose AI use
M18
Know when disclosure is required and how to phrase it.
Inform people they are interacting with an AI and own its use while affirming human validation. For low-level assistance, disclosure remains optional.
RegisterReady to equip your team?
Enroll your teams in our AI-literacy and governance training paths, from the common core to function-specific use cases.
Immediate access · 100% online · at your own pace