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Related Questions
- What are the key responsibilities of human annotators in evaluating the fairness of machine learning models?
- How do human annotators contribute to the transparency of machine learning models by identifying biases and errors?
- What are the potential consequences of inadequate human annotation in machine learning model development?
- Can human annotators be trained to recognize and address implicit biases in machine learning models?
- How do human annotators ensure that machine learning models are fair and transparent in real-world applications?
- What are some best practices for human annotators to follow when evaluating the fairness and transparency of machine learning models?
- How can human annotators collaborate with data scientists and engineers to develop more fair and transparent machine learning models?
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