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Related Questions
- How can overreliance on metrics like accuracy and F1-score lead to a lack of interpretability in AI models?
- What are some common pitfalls of using context-aware metrics that prioritize model performance over interpretability?
- How can the use of complex metrics like AUC-ROC and mean squared error lead to a lack of understanding of model behavior?
- What are some strategies for balancing model performance and interpretability when designing context-aware metrics?
- Can you provide examples of how prioritizing model performance over interpretability can lead to biased or unfair AI systems?
- How can the use of context-aware metrics that prioritize model performance over interpretability impact the reliability and trustworthiness of AI systems?
- What are some best practices for designing context-aware metrics that prioritize both model performance and interpretability?
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