Bias in AI
Bias in AI is systematic error or unfairness in how a model treats individuals or groups, often reflecting skewed data or flawed design. It can worsen existing inequalities if left unchecked.
In Simple Terms
Think of it as a tilted scale: the system consistently favors or disadvantages certain groups unless you correct for it.
Detailed Explanation
Bias can come from data (historical discrimination, underrepresentation, label noise), model design (features that proxy for protected attributes), or use (deploying in contexts where the model was not validated). Types include demographic bias, selection bias, and automation bias (over-trusting the system). Mitigation involves diverse and representative data, fairness metrics and testing, and human oversight. There is no one-size-fits-all definition of fairness; organizations must choose and document their criteria and trade-offs.
Related Terms
Artificial Intelligence
The simulation of human intelligence processes by machines, especially computer systems.
Read moreMachine Learning
A subset of AI that enables systems to learn and improve from experience without being explicitly programmed.
Read moreAI Safety
AI safety is the field focused on ensuring AI systems behave as intended, avoid harmful outcomes, and remain robust and controllable as capabilities scale. It spans technical research and practical governance.
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