Safety, Alignment & Ethics

Fairness

The goal of ensuring AI treats all people and groups equitably.

Definition

Fairness in AI refers to the principle that AI systems should not systematically disadvantage particular groups of people. This is harder to achieve than it sounds because 'fairness' can be defined in multiple mathematically incompatible ways — equal error rates across groups, equal outcomes, equal treatment of equal individuals. Regulatory pressure around AI fairness is growing, particularly in high-stakes applications like lending, recruitment, and healthcare.

Related Terms

Bias

Systematic unfairness in AI outputs, often reflecting imbalances in training data.

Transparency

The ability to understand and explain how an AI reaches its outputs.

Responsible AI

A framework for developing and deploying AI in ways that are ethical and accountable.

Heard enough terminology — ready to talk outcomes?

We translate AI concepts into measurable business results. No upfront fees — you pay only when independently verified results are delivered.

← Back to glossary

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

Knowledge Base·Safety, Alignment & Ethics·Fairness