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Related Questions
- Can feature importance plots highlight biased features that contribute to unfairness in e-commerce recommendations?
- How do partial dependence plots help identify interactions between features that lead to unfair outcomes in recommendation systems?
- Can high-dimensional feature spaces be effectively reduced using techniques like PCA or t-SNE to improve interpretability of fairness in e-commerce recommendations?
- What role do feature importance and partial dependence plots play in identifying and mitigating the impact of demographic bias in e-commerce recommendations?
- Can these plots help explain why certain features, such as age or location, have a disproportionate impact on recommendation outcomes?
- How do feature importance and partial dependence plots compare to other fairness metrics, such as disparity ratio or equal opportunity score, in capturing the impact of high-dimensional feature spaces?
- Can these plots be used to evaluate the fairness of e-commerce recommendations in real-time, as users interact with the system?
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