AI Fairness
The principle that AI systems should make decisions without unjust bias or discrimination against individuals or groups based on protected characteristics.
In plain language
Making sure AI treats everyone equally. For example, a hiring AI should evaluate candidates based on skills and experience, not give different results based on someone's name, gender or location.
Why this matters
Fairness failures create real business risk. Discriminatory AI outcomes can lead to lawsuits, regulatory action, customer loss and brand damage. Embedding fairness into your AI strategy is both an ethical imperative and a commercial necessity.
Relevance
ImplementationFairness testing and monitoring are essential implementation practices that prevent discriminatory outcomes and legal exposure.
Related terms
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