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Related Questions
- Can human evaluation detect biases that automated fairness metrics may miss?
- How do human evaluators assess fairness in machine learning models, and what are the limitations of their methods?
- In what ways can human evaluation challenge the results of automated fairness metrics, and what are the implications for model development?
- Can human evaluation provide a more nuanced understanding of fairness in machine learning, and if so, how?
- How do human evaluators balance the need for fairness with other competing goals, such as model accuracy or efficiency?
- Can human evaluation be used to identify and mitigate biases in machine learning models that are not detected by automated fairness metrics?
- What are the potential drawbacks of relying solely on human evaluation to assess fairness in machine learning, and how can these be mitigated?
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