Insufficient Diversity Among Data Curators and Annotators

The lack of gender diversity among those who collect and label data can lead to blind spots in recognizing and mitigating gender bias. Diverse teams are more likely to identify and correct biases in datasets, whereas homogenous groups may unintentionally perpetuate them.

The lack of gender diversity among those who collect and label data can lead to blind spots in recognizing and mitigating gender bias. Diverse teams are more likely to identify and correct biases in datasets, whereas homogenous groups may unintentionally perpetuate them.

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AI and Bias: How Gender Affects Algorithms

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