Glossary · 6 · Quality, risk and governance
Bias (AI)
Also known as: Algorithmic bias, Fairness
Bias in AI is a systematic skew in a model’s outputs — favoring or disadvantaging certain groups, viewpoints, languages or cases — that usually stems from imbalances in its training data, its design or the way it is used.
- Beginner
- Technical writers
- Technical marketers
- Technical project managers
In one sentence
AI bias explained: systematic skews in AI output, where they come from, and how writers, marketers and PMs can spot and reduce them.
Example
An image generator asked for “an engineer at a machine” mostly shows men; a marketing team specifies diverse people in its prompts and reviews results.
Why it matters on your learning path
- Technical writers: Check AI-generated examples, names and images for stereotypes, as you would with any content.
- Technical marketers: Biased imagery or copy damages brands; review campaigns for representation.
- Technical project managers: For AI that affects people’s access to jobs, credit or services, bias testing is a legal topic — see high-risk AI system.