Answer / Ateequr Rehman

Handling bias that is deeply embedded in the training data can be addressed through various techniques, such as: (1) Data preprocessing to remove or reduce biased features; (2) Utilizing multiple datasets to capture a more diverse range of representations; (3) Collecting new, unbiased data to replace biased data; (4) Using algorithms that are robust to adversarial examples and can handle outliers; and (5) Implementing fairness-aware machine learning techniques that prioritize fairness during the model training process.

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