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How would you handle bias when it is deeply embedded in the training data?
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Answer Posted / 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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