Gender bias in algorithms is more than a technical flaw; it’s a reflection of deep-seated societal biases that inadvertently find their way into technological creations. This bias is not always overt but hidden in the data sets used for training algorithms, leading to outcomes that favor one gender over another. Addressing this requires a multidisciplinary approach, merging ethical oversight with technological advancements to root out biases from the onset of algorithm design.
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