The involvement of diverse groups in the data annotation process can help reduce gender bias in tech. When people from various gender identities participate in labeling or annotating data, their perspectives help mitigate biases that automated systems or less diverse teams might introduce. This empowerment of underrepresented voices ensures that the resulting datasets—and thus the algorithms trained on these datasets—reflect a broader range of human experiences and values.
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