Bias Detection

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Bias detection focuses on identifying and reducing unfair differences in how AI models make predictions across different groups or contexts. It starts by analyzing performance metrics for various categories such as gender, age, region, or device type (always following ethical and legal guidelines). These comparisons help reveal whether the model treats some groups less fairly than others.

Once bias is found, teams investigate its source, which can stem from unbalanced training data, mislabeled examples, or historical inequalities reflected in the data. Bias detection also involves checking for missing representations or irrelevant patterns that cause systematic errors.

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