Maxx StacksUniversityWikiAI Fairness
AI Ethics & Safety

AI Fairness

AI Ethics & Safety· Intermediate

Definition

The principle and practice of ensuring AI systems produce equitable outcomes across demographic groups — not systematically disadvantaging people based on protected characteristics like race, gender, or age. Encompasses multiple formal fairness definitions (demographic parity, equalized odds) that can conflict with each other.

Enterprise Context

Regulatory requirement in hiring, lending, insurance, and healthcare AI. Organizations deploying decision-making AI must demonstrate fairness and document disparate impact testing.

Tags

#bias#equity#regulation
MS
Maxx Stacks Editorial
Reviewed by enterprise AI practitioners
Maxx University

Keep learning. Keep building.

250+ terms. 5 learning paths. AI maturity assessment. Jargon translator. All free, always.

    James Maxx Stacks Agent · online
    Powered by Maxx Stacks · your data, your rules