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Related Questions
- Can human annotators directly correct biases in labels without retraining the model?
- Does correcting bias in labels require retraining the model or can it be done manually?
- Can annotators update existing labels to correct biases without affecting the model's performance?
- What are the limitations of correcting bias in labels manually, and when does retraining become necessary?
- How can annotators ensure that their corrections do not introduce new biases in the model?
- Can correcting bias in labels manually lead to overcorrection or undercorrection, and how can this be avoided?
- What is the best approach to correcting bias in labels: manual correction, retraining the model, or a combination of both?
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