This content originally appeared on DEV Community and was authored by Carlos Ruiz Viquez
Unlocking Explainability Transparency with SHAP Values: A Code Snippet for AI Governance
In the pursuit of building trustworthy AI systems, explainability and transparency have become essential components of AI governance. One key technique for achieving these goals is the use of SHAP (SHapley Additive exPlanations) values. SHAP values provide a way to assign importance scores to individual input features, helping to illuminate the decision-making process of complex AI models.
Code Snippet: SHAP-Enabled Explainability
Below is a Python code snippet that demonstrates how to leverage SHAP values to generate feature importance for an AI model.
python
import shap
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
class AIModel:
def __init__(self, model):
self.model = model
def explain(self, input_data):
# Use SHAP values to generate feature importance
...
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This content originally appeared on DEV Community and was authored by Carlos Ruiz Viquez
Carlos Ruiz Viquez | Sciencx (2025-09-22T19:59:16+00:00) **Unlocking Explainability Transparency with SHAP Values: A. Retrieved from https://www.scien.cx/2025/09/22/unlocking-explainability-transparency-with-shap-values-a/
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