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Related Questions
- How can human annotators identify and address biases in training data through active learning?
- What are the key strategies for human annotators to employ when selecting and annotating data for active learning?
- Can active learning be used to reduce the amount of data required for training while maintaining model accuracy?
- How does active learning impact the overall performance of machine learning models in terms of fairness and bias?
- What are some common pitfalls or challenges that human annotators may face when using active learning to address biased data?
- Can active learning be used in conjunction with other techniques, such as data preprocessing or model regularization, to address biased data?
- How can human annotators evaluate the effectiveness of active learning in reducing bias in training data?
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