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Bias and Fairness in AI
Bias in AI refers to systematic errors or prejudices embedded in an AI system's outputs, often reflecting historical or societal biases present in the training data. This can lead to unfair or discriminatory outcomes for certain groups. Achieving fairness involves designing algorithms and collecting data in ways that mitigate these biases and ensure equitable treatment.
In plain terms
If you train a chef only on recipes from one cuisine, they'll be biased towards those flavors and may not cook other dishes well, even if they have perfect technique.
Why it matters
Ensuring fairness is critical for the ethical deployment of AI, preventing harm, and building trust in automated systems.
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