Popular GitHub Repositories for Interpretable Ai

PyTorch Explain: Interpretable Deep Learning in Python.

A curated list of awesome academic research, books, code of ethics, data sets, institutes, maturity models, newsletters, principles, podcasts, reports, tools, regulations and standards related to Responsible, Trustworthy, and Human-Centered AI.

Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.

Interpretable Pre-Trained Transformers for Heart Time-Series Data

Find the samples, in the test data, on which your (generative) model makes mistakes.

[TAAI'21] Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud Detection

[Frontiers in AI Journal] Implementation of the paper "Interpreting Vision and Language Generative Models with Semantic Visual Priors"

Interpretability and Fairness in Machine Learning

Neural Additive Models - Visualization Tool in PyTorch/Plotly-Dash

JAX-based Model Explanation and Interpretation Library

Playing around with understanding how different tokens affect the generation in LLMs

Webapp/Application implemention of my thesis about XAI and Interpretability of Transformer Models.

SHAP explainer for LightGBM models - Generate feature importance plots, dependence plots, and prediction explanations with one line of code. Make your gradient boosting models interpretable for stakeholders.