Why Attribution Stability Matters More Than Attribution Accuracy
📰 Hackernoon
Learn why attribution stability is more crucial than attribution accuracy in regulated AI and how to measure it using σ_SHAP
Action Steps
- Calculate SHAP values for your model using a library like SHAP
- Compute σ_SHAP by taking the variance of SHAP values across K rotated background samples
- Use σ_SHAP as a metric to evaluate the stability of your model's attributions
- Compare the stability of different models using σ_SHAP
- Optimize your model to improve attribution stability as measured by σ_SHAP
Who Needs to Know This
Data scientists and AI engineers working on regulated AI projects will benefit from understanding the importance of attribution stability and how to measure it
Key Insight
💡 Attribution stability, measured by σ_SHAP, is a more important metric than attribution accuracy for regulated AI
Share This
🚨 Attribution stability > attribution accuracy in regulated AI! 🚨 Use σ_SHAP to measure stability
Key Takeaways
Learn why attribution stability is more crucial than attribution accuracy in regulated AI and how to measure it using σ_SHAP
Full Article
SHAP attribution accuracy is the wrong metric for regulated AI. σ_SHAP — variance across K rotated background samples — is the defensible alternative.
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