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Related Questions
- What are the different types of bias that can occur in Qwen's training data?
- How can I use fairness metrics such as disparate impact and equal opportunity to audit Qwen's training data?
- What are some tools and frameworks available for detecting and mitigating bias in Qwen's training data?
- How can I use techniques such as data augmentation and debiasing word embeddings to improve the fairness of Qwen's training data?
- What is the role of contextual bias in Qwen's training data, and how can I detect and address it?
- Can I use techniques from computational linguistics, such as style transfer and paronym substitution, to identify biased text in Qwen's training data?
- What are some challenges associated with auditing the fairness of Qwen's training data in real-world settings?
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