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Related Questions
- How do model interpretability techniques, such as feature importance and partial dependence plots, help identify biases in machine learning models?
- Can you explain how to use model interpretability to detect and mitigate unfair biases in decision-making systems?
- How do techniques like SHAP values and LIME help to understand how individual predictions are made, and how can this information be used to improve fairness?
- What is the relationship between model interpretability and explainability, and how do they contribute to fairness in machine learning?
- Can you provide examples of how model interpretability has been used to improve fairness in real-world applications, such as credit scoring or hiring systems?
- How can we use model interpretability to identify and address disparate impact, and what are some best practices for doing so?
- What role does model interpretability play in ensuring that machine learning models are transparent and accountable, and how can this contribute to fairness?
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