Tag: xAI

All the talks with the tag "xAI".

• GNNX-BENCH - Perturbation-based GNN Explainers

  Rishi Raj Sahoo
  Published:Nov 11, 2024 at  at 02:00 PM

  This work presents a benchmark for Graph Neural Network Explainability with perturbation. This involves some metrics like Sufficiency, Necessity, Stability, and Reproducibility to observe which method performs better. Finally, the work provides recommendations for choosing methods for particular graph tasks.

• Balancing Explainability-Accuracy of Complex Models

  Poushali Sengupta
  Published:Oct 21, 2024 at  at 03:45 PM

  This talk covers the Explainability through Correlation Impact Ratio (ExCIR) method, which 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. The method reliably ranks feature importance in both original and simplified models, validating its consistency.