The data used to train AI systems often reflects existing societal biases, including gender biases. To combat this, it's essential to carefully curate and regularly audit training datasets to ensure they are representative and free from harmful stereotypes. This requires a concerted effort from both male and female developers, ethicists, and domain experts to scrutinize and adjust the data inputs and training processes.
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