One of the key challenges in ensuring fairness in AI is the bias present within the data used to train algorithms. Biased data can lead to biased outcomes, reinforcing and perpetuating stereotypes. To overcome this, it is crucial to implement robust data auditing practices to identify and correct biases, employing diverse and inclusive datasets that accurately reflect the diversity of the real world.
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