Safety, Alignment & Ethics
Bias
Systematic unfairness in AI outputs, often reflecting imbalances in training data.
Definition
AI bias refers to systematic patterns in AI outputs that are unfair or inaccurate due to biases in the training data or model design. Because AI learns from human-generated content, it can absorb and amplify human prejudices — associating certain professions with specific genders, underperforming for non-Western names, or producing culturally narrow outputs. Bias can be subtle and context-dependent, making it difficult to detect without deliberate testing across diverse inputs.
Related Terms
Fairness
The goal of ensuring AI treats all people and groups equitably.
Training Data
The text, documents, or other content an AI learns from during its development.
AI Safety
The field focused on preventing AI from causing harm — intentional or unintentional.
Transparency
The ability to understand and explain how an AI reaches its outputs.
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Disclaimer
This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.
Document reference: ISO_webpage_knowledge-base_glossary_v1
Last modified: 29 March 2026