How do AI Engineers deal with bias in AI models?
The question is about AI Engineering .
Bias mitigation isn't a one-time fix; it's an ongoing practice engineers apply more or less intensively depending on how skewed the training data is and how much the model's output affects people. Equalized odds, one of the standard fairness metrics, requires that a model's true positive and false positive rates match across groups, and it can conflict with other fairness metrics measured on the same model, so improving one can worsen another.
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