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Related Questions
- What are some methods used by human annotators to evaluate the transparency and explainability of machine learning models?
- How do human annotators identify and mitigate bias in machine learning models during the annotation process?
- What are the potential consequences of a lack of transparency and explainability in machine learning models on fairness and bias?
- Can human annotators use techniques such as feature attribution or model interpretability to improve the transparency of machine learning models?
- How do human annotators ensure that machine learning models are fair and unbiased, and what are the challenges associated with achieving fairness and bias?
- What role do human annotators play in identifying and addressing potential biases in machine learning data and models?
- What are some best practices for human annotators to follow when working with machine learning models to ensure transparency, explainability, and fairness?
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