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Related Questions
- What are the key fairness metrics used to evaluate AI models, and how do they differ from traditional accuracy metrics?
- Can you explain the concept of disparate impact and how it relates to fairness in AI model evaluation?
- How do evaluation methods like fairness auditing and bias detection help identify and mitigate fairness issues in AI models?
- What are some common pitfalls in using fairness metrics and evaluation methods, and how can they be avoided?
- How can fairness metrics and evaluation methods be used to compare the performance of different AI models and identify best practices?
- What role do human evaluators play in assessing fairness in AI models, and how can their input be integrated into evaluation methods?
- Can you discuss the trade-offs between fairness and other important AI model performance metrics, such as accuracy and efficiency?
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