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Fairness

As explained in Introduction to Generative AI, Second Edition

Fairness is the property of treating individuals and groups equitably in an algorithmic system's design, operation, and outcomes.

Fairness is the effort to treat individuals and groups equitably through an algorithmic system’s design, operation, and outcomes. It forms part of responsible AI alongside transparency, privacy, human oversight, and regulatory compliance. section 10.33

Fairness depends on governance rather than technical design alone. Voluntary company principles have often fallen short, while early-2020s policy discussions increasingly emphasize accountability and comprehensive regulation. Because AI’s effects are global, fairness debates also face concerns about whose interests and perspectives are represented. section 10.33

How Introduction to Generative AI, Second Edition covers it

How deep it goes
Frames fairness as a governance concern but stops short of defining tests, metrics, or operational procedures for measuring equitable treatment.
Examples
Contrasts voluntary AI-company self-policing with comprehensive regulation and highlights globally uneven participation in AI policy discussions.
How it connects
Connects fairness with AI governance, algorithmic transparency, privacy, data protection, human oversight, accountability, and regulation.

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