To detect gender bias in training data, it's essential to scrutinize the dataset for representation disparities, biased labeling practices, and imbalance in gender portrayal within data samples. Analyzing linguistic patterns, image annotations, and the context in which genders are represented can uncover subtle biases. Tools and frameworks designed for bias detection can assist in this analysis, providing a quantitative basis for identifying areas where gender representation is not equitable.
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