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Related Questions
- Can fairness metrics help identify biases in word embeddings and reduce their impact on downstream AI models?
- How do audit tools contribute to detecting and mitigating bias in word embeddings, and what types of biases can they identify?
- What are some common fairness metrics used to evaluate the bias in word embeddings, and how do they work?
- Can bias in word embeddings be addressed through techniques like debiasing or regularization, and what are their limitations?
- How do word embeddings learn biases, and what are some potential sources of bias in their training data?
- Can word embeddings be used to detect and mitigate bias in other AI models, such as classification or recommendation systems?
- What are some potential consequences of biased word embeddings on AI decision-making and outcomes, and how can they be mitigated?
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