Model Explainability

Model explainability refers to techniques and methods that make machine learning models more transparent and interpretable, allowing humans to understand how models make predictions or decisions. It involves explaining model behavior, feature importance, and decision logic, often through visualizations, metrics, or simplified surrogate models. This is crucial for building trust, ensuring fairness, and meeting regulatory requirements in AI systems.

Also known as: Explainable AI, XAI, Model Interpretability, AI Transparency, Interpretable Machine Learning

Why learn Model Explainability?

Developers should learn model explainability when deploying machine learning models in high-stakes domains like healthcare, finance, or autonomous systems, where understanding model decisions is critical for safety, ethics, and compliance. It helps debug models, identify biases, improve performance, and communicate results to non-technical stakeholders, especially under regulations like GDPR or in industries requiring auditability.

Learning Resources

• book: Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
• docs: Google's Explainable AI Documentation
• course: Coursera: Interpretable Machine Learning
• docs: SHAP (SHapley Additive exPlanations) GitHub Repository
• video: YouTube: Introduction to Explainable AI by Google Developers