responsible-ai-workshop

A game theoretic approach to explain the output of any machine learning model.

? Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

A collection of research papers and software related to explainability in graph machine learning.

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

Interpretability and explainability of data and machine learning models

Model explainability that works seamlessly with ? transformers. Explain your transformers model in just 2 lines of code.

Explainability for Vision Transformers

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

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