Summary of Explainable Ai Through a Democratic Lens: Dhondtxai For Proportional Feature Importance Using the D’hondt Method, by Turker Berk Donmez
Explainable AI through a Democratic Lens: DhondtXAI for Proportional Feature Importance Using the D’Hondt Method
by Turker Berk Donmez
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Digital Libraries (cs.DL); Machine Learning (cs.LG)
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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 study proposes DhondtXAI, a novel approach to Explainable AI (XAI) that integrates democratic principles of proportional representation. Inspired by the D’Hondt method, DhondtXAI leverages resource allocation concepts to interpret feature importance within AI models. The authors compare DhondtXAI with SHAP (Shapley Additive Explanations) for CatBoost and XGBoost models in breast cancer and diabetes prediction tasks, respectively. DhondtXAI’s alliance formation and thresholding capabilities enhance interpretability, visualizing feature importance as seats in a parliamentary view. Statistical correlation analyses demonstrate consistency between SHAP values and DhondtXAI allocations, highlighting the potential of DhondtXAI as a complementary tool for understanding feature importance in AI models. This work showcases how electoral principles can improve user understanding, particularly in high-stakes fields like healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making machine learning more understandable by using ideas from democracy. They propose a new way to explain why an AI model makes certain predictions, called DhondtXAI. This method is inspired by how democratic countries elect representatives and aims to make AI decisions more transparent. The researchers compare this approach with another popular explanation method, SHAP, on several different datasets. Their results show that DhondtXAI can help us understand why an AI model chooses certain features as important, which can be especially helpful in areas like healthcare where the stakes are high. |
Keywords
» Artificial intelligence » Machine learning » Xgboost
