What is Bias in AI?
Results that are systematically unfair to some people or groups.
In plain English
Bias often comes from the data: if training data under-represents a group or reflects past unfair decisions, the model learns it. It can also come from how a problem is defined or measured. Checking for bias means comparing outcomes across groups, improving the data, and monitoring the system after launch.
Example
A model trained mostly on photos of one skin tone performs worse for others.
Easy to confuse with
- Hallucination: Bias is unfairness. A hallucination is an invented fact.
Where you will meet it
This idea belongs to the subject area of these practice tests. We do not publish or predict real exam questions.
- IAPP AIGP practice tests
- ISACA AI Fundamentals practice tests
- Microsoft AI-901 practice tests
- PMI-CPMAI practice tests
Related terms
- Responsible AI: Building and using AI in ways that are fair, safe, private, transparent and accountable.
- Overfitting: When a model learns its training data too closely and does badly on new data.
- Model drift: A model getting worse over time because the real world no longer looks like its training data.
Test yourself
Practice tests with an explanation for every answer option, so each mistake teaches you something.
Browse practice testsAll glossary termsWritten and checked by the Be Ready Education team, last reviewed 2 November 2026. General study information, not professional advice. Providers change names and rules, so confirm details on the official documentation. Not affiliated with, endorsed by or sponsored by any certification vendor or exam body. Certification names, exam codes and product names are trademarks of their respective owners and are used only to identify the exam a practice product prepares for. Our questions are original and are not actual exam questions.