← Concept IndexDEFINITION WHY IT MATTERS COMMONLY CONFUSED WITH SOURCES
Bias, fairness, and representation
Also called: stereotyping, discrimination
Models learn patterns from human data, including its biases, and can reproduce or amplify them — in who gets described how, whose language is treated as default, and which outcomes a system recommends.
When AI shapes hiring, lending, images, or advice, biased output causes real-world harm at scale and can be unlawful. Noticing whose perspective is missing is part of using it responsibly.
A fixable glitch. Bias isn't a single bug to patch; it's a property of the data and the use, managed through testing, diverse review, and human judgment.
- Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1U.S. National Institute of Standards and Technology · 2023-01-26
- Recommendation on the Ethics of Artificial IntelligenceUNESCO · 2021-11-23
First-pass citations, limited to primary sources; a reviewer will broaden and verify these before this entry leaves draft.