Exploring Explainable AI (XAI) for Decision-Making
Shiva Ganesh
LIME (Local Interpretable Model-agnostic Explanations) helps in understanding individual predictions by approximating the model locally with an interpretable one.
SHAP (SHapley Additive exPlanations) provides consistent and locally accurate feature importance values, making it easier to interpret complex models.
IBM Watson OpenScale offers tools to monitor and explain AI models, ensuring they are fair, transparent, and accountable.
Google Cloud AI Explanations provides detailed insights into model predictions, helping users understand how different features impact outcomes.
Microsoft Azure Machine Learning Interpretability includes a suite of tools to help developers and data scientists interpret model predictions and improve transparency.