Explainability in AI is crucial across various applications, yet existing methods often overlook the explainability-accuracy trade-off in complex models. We introduce Explainability through Correlation Impact Ratio (ExCIR), a novel approach that balances explainability and accuracy in complex machine learning models, especially with independent features. ExCIR creates a streamlined data space from the original dataset, requiring fewer input samples while maintaining model accuracy. The Correlation Impact Ratio (CIR) quantifies each feature’s contribution to the model’s output, accounting for feature uncertainty regarding its presence or absence. Using the CAUEEG dataset related to dementia, we demonstrate that ExCIR maintains accuracy while enhancing explainability through feature impact scores. Our method reliably ranks feature importance in both original and simplified models, validating its consistency.
Speaker’s Profile: Poushali Sengupta - Department of Informatics