AI Bias
Systematic errors in AI outputs resulting from prejudiced training data or flawed algorithms, leading to unfair outcomes.
Systematic deviations in AI outputs that unfairly favor or disadvantage particular groups - stemming from skewed datasets, flawed labeling, or mis-specified objectives - and requiring detection, measurement, and mitigation.
A facial-recognition system trained mostly on light-skinned faces shows higher error rates for darker-skinned individuals. The vendor rebalances its training dataset and deploys ongoing bias-monitoring dashboards to ensure equitable performance across all skin tones.
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What problem does Enzai solve?
Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
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How is Enzai different from other governance tools?
Can we start if we have no existing AI governance process?
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