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Explain the importance of ethical considerations in AutoML. Discuss how AutoML tools address potential biases in data and models. Provide examples of techniques used to ensure fairness and transparency in AutoML.
A
Bias detection, fairness metrics, and explainable AI.
B
Data anonymization, differential privacy, and secure multi-party computation.
C
Model regularization, dropout layers, and adversarial training.
D
Data encryption, access control, and audit logs.
