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Answer: Bias detection, fairness metrics, and explainable AI.
Ethical considerations are paramount in AutoML to ensure that models are fair, transparent, and free from biases. AutoML tools address potential biases through techniques such as bias detection, which identifies and quantifies biases in the data or model predictions, fairness metrics, which assess the model's performance across different subgroups, and explainable AI, which provides insights into the model's decision-making process. These techniques help ensure that AutoML models are developed and deployed responsibly, with a focus on fairness and transparency.
Author: LeetQuiz Editorial Team
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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.
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