Critical thinking about AI
Detecting bias
Notice when an AI answer treats groups of people unfairly or presents a partial view as neutral.
Models learn from data that reflects past choices and imbalances. Their answers can repeat stereotypes, favour one group or present one side of a question as the only one, while sounding neutral.
In recruitment, credit, customer service or internal communication, a biased answer can discriminate, hurt people and expose the company legally. The risk grows when the answer is reused at scale without review.
Ask who could be disadvantaged by the answer, try the same request with different profiles and look for missing points of view. A biased result is not a reason to give up on AI, but a reason to review and correct before use.
In the reports of your games, every answer linked to this skill counts: the skill is shown as acquired from 80% of correct answers, and in progress from 50%.
Games that train this skill
Play them for free, without an account, then adapt them for your teams.
