Data Ethics, Algorithmic Bias & Privacy

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Identify historical training bias, ensure fairness in algorithmic decision systems, and comply with GDPR privacy standards.

Mitigating Algorithmic Bias

Models reflect historical patterns in training data. If historical hiring decisions favored certain demographics, an unmonitored model will replicate and amplify that bias. Data practitioners must audit disparity metrics across demographic groups.

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Test Yourself With Exercise

+15 XP

Can training an algorithm on historical real-world data inadvertently encode and automate demographic bias?

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