Summary of Counterfactual Explanations Of Black-box Machine Learning Models Using Causal Discovery with Applications to Credit Rating, by Daisuke Takahashi et al.
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating
by Daisuke Takahashi, Shohei Shimizu, Takuma Tanaka
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The novel XAI framework relaxes the constraint that the causal graph is known, leveraging counterfactual probabilities and prior information on causal structure to integrate a causal graph estimated through causal discovery methods with a black-box classification model. This approach enables more accurate estimation of explanatory scores based on counterfactual probabilities. Numerical experiments demonstrate improved performance over traditional methods. The framework’s effectiveness is further demonstrated in an application to real data, specifically credit ratings assigned by Shiga Bank. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way for artificial intelligence (AI) to explain its decisions. Most AI models are like black boxes – we don’t know how they make their predictions. This new method helps figure out the relationships between different things that affect an AI’s decision. It does this by looking at what would happen if something were different, and then using that information to understand why the AI made a certain choice. The team tested this approach with fake data and found it worked better than other methods. They also applied it to real credit ratings from Shiga Bank in Japan and showed that it can be useful. |
Keywords
* Artificial intelligence * Classification
