AI technologies are not immune to the biases of their creators. When AI systems are trained on data that reflects historical or societal gender biases, these prejudices can get embedded into the technology itself. This may lead to women receiving less relevant job recommendations, encountering gendered digital assistants that reinforce stereotypes, or facing higher rejection rates in automated hiring processes. Addressing these biases requires a concerted effort to diversify AI training data and the teams that build these technologies.
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