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Related Questions
- What are some common challenges associated with implementing active learning in bias mitigation, such as selecting the most informative samples or ensuring sufficient human annotation?
- How do the limitations of human annotators, such as cognitive biases and variability, impact the effectiveness of active learning in identifying and mitigating bias?
- Can active learning be used to address bias in datasets with complex or nuanced biases, or is it more suited to detecting overt or obvious biases?
- What are some potential trade-offs between the cost and benefits of active learning in bias mitigation, such as the need for human annotation versus the potential for improved model performance?
- How can active learning be combined with other techniques, such as data preprocessing or model selection, to more effectively identify and mitigate bias in large datasets?
- What are some potential risks or unintended consequences of using active learning to identify and mitigate bias, such as overfitting or introducing new biases?
- Can active learning be used to address bias in datasets with changing or dynamic distributions, or is it more suited to static datasets?
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