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Related Questions
- How can active learning help mitigate the risk of biased annotations in large datasets?
- What strategies can be employed to ensure that active learning algorithms are fair and transparent in their data selection and annotation processes?
- Can you explain the importance of human oversight in active learning for annotating sensitive topics, such as demographics or politics?
- How do active learning methods account for the potential biases introduced by human annotators, and what techniques can mitigate these biases?
- What are some best practices for implementing active learning in a way that promotes fairness and reduces bias in data annotation?
- How can active learning be used to detect and correct biases in existing datasets, particularly in areas like natural language processing?
- What are some common pitfalls to avoid when using active learning for annotating sensitive topics, and how can they be addressed?
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