Token Standards (Global)
High-quality technical overview of Token Standards in the context of blockchain security.
Techniques: 1. Feature Importance. 2. Partial Dependence Plots. 3. Saliency Maps. 4. LIME/SHAP. 5. Attention Visualization.
graph LR
Center["Token Standards (Global)"]:::main
Rel_token_standard["token-standard"]:::related -.-> Center
click Rel_token_standard "/terms/token-standard"
Rel_token_security["token-security"]:::related -.-> Center
click Rel_token_security "/terms/token-security"
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🧒 5歳でもわかるように説明
Imagine two math students. Student A gives you the right answer but won't show you how they did it. Student B gives you the answer and draws a map showing every step they took. Interpretable AI is Student B: it doesn't just give you an answer; it explains its [logic](/ja/terms/logic) so you can trust it.
🤓 Expert Deep Dive
Technically, interpretability is divided into 'Glass-box' models (inherently simple, like Decision Trees or GLMs) and 'Post-hoc' explanations for 'Black-box' models. Tools like 'SHAP' use game theory to assign a value to each input feature, showing how much it contributed to the final prediction. Another technique is 'Saliency Mapping' in computer vision, which highlights the pixels in an image that the AI was looking at when it identified a 'cat'. The 'Interpretability-Accuracy Trade-off' remains a core challenge: usually, the more accurate a model becomes (by adding more layers and parameters), the harder it is for humans to interpret its internal state.