Beginner Explanation
Imagine you have a really smart robot that can make decisions for you, like picking the best route to avoid traffic. But sometimes, it makes choices that seem strange, like taking a longer road. Explainable AI is like asking the robot, ‘Why did you choose that route?’ It helps the robot explain its reasoning in a way you can understand, just like a friend explaining why they prefer one movie over another. This way, you can trust its decisions more because you know the logic behind them.Technical Explanation
Explainable AI (XAI) encompasses techniques that allow machine learning models to provide human-understandable justifications for their predictions. Common methods include LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). For example, using SHAP in Python can be done as follows: “`python import shap import xgboost as xgb # Load data and train a model X, y = shap.datasets.boston() model = xgb.XGBRegressor().fit(X, y) # Create SHAP explainer and compute SHAP values explainer = shap.Explainer(model) shap_values = explainer(X) # Visualize the SHAP values shap.summary_plot(shap_values, X) “` This code snippet shows how to use SHAP to explain the predictions of an XGBoost model, providing insights into feature importance and model behavior.Academic Context
Explainable AI (XAI) is a burgeoning field aimed at enhancing the transparency and interpretability of AI systems. Theoretical frameworks such as Shapley values from cooperative game theory provide a basis for many XAI methods, including SHAP. Key papers include ‘Why Should I Trust You?’ Explaining the Predictions of Any Classifier by Ribeiro et al. (2016), which introduces LIME, and ‘A Unified Approach to Interpreting Model Predictions’ by Lundberg and Lee (2017), which presents SHAP. XAI is critical for applications in high-stakes domains like healthcare, finance, and autonomous systems, where understanding AI decision-making is essential.Code Examples
Example 1:
import shap
import xgboost as xgb
# Load data and train a model
X, y = shap.datasets.boston()
model = xgb.XGBRegressor().fit(X, y)
# Create SHAP explainer and compute SHAP values
explainer = shap.Explainer(model)
shap_values = explainer(X)
# Visualize the SHAP values
shap.summary_plot(shap_values, X)Example 2:
import shap
import xgboost as xgb
# Load data and train a model
X, y = shap.datasets.boston()Example 3:
import xgboost as xgb
# Load data and train a model
X, y = shap.datasets.boston()
model = xgb.XGBRegressor().fit(X, y)View Source: https://arxiv.org/abs/2511.16482v1