Women in tech can begin by deeply understanding different forms of bias—such as sampling bias, confirmation bias, or measurement bias—that can occur in datasets or algorithms. This foundational knowledge enables them to better identify where biases might be introduced during data collection, preprocessing, or model training, and take deliberate actions to correct these issues.
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