Model Agnostic Explainability

Model-agnostic explainability (MAE) focuses on understanding the predictions of any machine learning model, regardless of its internal workings, to improve transparency and trust. Current research emphasizes developing techniques that provide feature importance scores, contrastive explanations (showing what would change a prediction), and human-in-the-loop approaches to validate explanations, often leveraging methods like SHAP values, LIME, and Taylor expansions. These advancements are crucial for deploying machine learning models responsibly in high-stakes domains like healthcare and finance, where understanding model decisions is paramount. MAE methods are being adapted for various applications, including time series forecasting, document image classification, and social media analysis.