Summary of Cage: Causality-aware Shapley Value For Global Explanations, by Nils Ole Breuer et al.
CAGE: Causality-Aware Shapley Value for Global Explanations
by Nils Ole Breuer, Andreas Sauter, Majid Mohammadi, Erman Acar
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new approach for explaining AI models, specifically for predicting the importance of input features in machine learning (ML) models. By leveraging Shapley values, the authors introduce CAGE (Causally-Aware Shapley Values for Global Explanations), which takes into account causal relations between input features. This method is designed to provide more accurate and intuitive global explanations compared to existing methods. The paper demonstrates its effectiveness on both synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI models are becoming increasingly important in our daily lives, but it’s crucial that their decisions are transparent and explainable. One way to do this is by understanding how the model uses each input feature. Shapley values can help with this, but existing methods often assume features don’t affect each other. This paper introduces CAGE, a new approach that considers these causal relationships. It uses a sampling procedure to select the most important features and provides more accurate explanations. |
Keywords
* Artificial intelligence * Machine learning
