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Related Questions
- What are the common pitfalls in data collection and annotation that can lead to biased NLP models?
- How can developers detect and mitigate bias in NLP models without relying on manual auditing or expert judgment?
- What are the trade-offs between model performance and fairness in NLP applications, and how can they be balanced?
- What are some strategies for incorporating diverse perspectives and feedback into the NLP model development process?
- How can NLP models be designed to handle out-of-distribution data and mitigate the risk of biased predictions?
- What are the challenges in deploying bias-mitigated NLP models in real-world applications with limited computational resources and data availability?
- Can you provide examples of successful bias-mitigation techniques in NLP models and their limitations?
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