Algorithmic bias is a systematic and unfair skew in an AI system’s outputs, often caused by imbalanced training data, that results in different outcomes for different groups of people.
Why It Matters
Bias in an AI system used for consequential decisions, employment, lending, healthcare, can create real legal and reputational exposure, and is a specific focus of emerging AI regulation that requires organizations to actively test for and address it.
A Practical Example
An AI-driven loan approval model trained primarily on historical data from one demographic group produces systematically lower approval rates for applicants outside that group, even when their financial profiles are comparable.
Related Terms
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