Explainable AI: Unlock the 'black box' of AI models

Explore what explainable AI is, how it clarifies global and local model behavior, highlights feature influence, and supports trust, fairness, and compliance in high-stakes applications.

Explore explainable AI by examining rule based models, consolidated score, generalized additive model, prototype models, and attention based models to highlight interpretability and varying prediction approaches.

Explore global and local interpretability in explainable AI by examining an employee churn model, revealing how monthly income and training times last year influence overall versus data-point level decisions.

Explore coefficient interpretation and future importance in tree-based models. Examine model-agnostic and gradient-based techniques such as shap, lime, ice plots, and integrated gradients.

Explore global explainability of a logistic regression churn model by interpreting feature coefficients as log odds and identifying key numeric drivers of employee churn.

Explore global explainability for decision trees in churn prediction, computing feature importance with Gini impurity and visualizing key drivers like monthly income.

Explain how shape and line provide local and global interpretability for any model, compare Shapley-based contributions with Lime's perturbation approach, and discuss computational trade-offs.

Explain how to interpret anomaly detection outcomes in motor insurance fraud using isolation forest and shap to identify driving factors and guide preventive controls.

Explore global and local explainability for supervised learning with Shap values in a random forest ensemble, using an employee churn case study to reveal which features drive outcomes.

Explore how shape values reveal feature contributions to model predictions using Shapley values from game theory, visualized in summary and dependence plots, with a local weighted regression explanation.

Explainable ai session uses shap to explain an xgboost regression model predicting insurance charges, focusing on numeric factors age, bmi, and children, with age shown as the most influential.

Explain how counterfactual explanations reveal the smallest feature changes to flip a model’s prediction, using churn prediction and the dice library for actionable, local interpretability in explainable AI.