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Related Questions
- How can human evaluators identify biases in machine learning models that are not immediately apparent through automated testing?
- What are some common pitfalls that human evaluators should be aware of when evaluating machine learning models for bias?
- Can you provide examples of successful human oversight and evaluation strategies for mitigating biases in machine learning models?
- What is the importance of diverse and representative human evaluation teams in identifying and mitigating biases in machine learning models?
- How can human evaluators effectively communicate their findings and recommendations for bias mitigation to stakeholders and developers?
- What are some best practices for incorporating human oversight and evaluation into the machine learning development lifecycle?
- Can you discuss the role of human evaluators in ensuring that machine learning models are fair and transparent, and how this relates to explainability and accountability?
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