Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

All about explainable AI, algorithmic fairness and more

User documentation for KServe.

In the wild extraction of entities that are found using Flair and displayed using a very elegant front-end.

Counterfactual SHAP: a framework for counterfactual feature importance

XAI-Analytics is a tool that opens the black-box of machine learning. It helps the user to understand the decision-making process of machine learning models.

Generating global explanations from local ones

Explainable AI: From Simple Rules to Complex Generative Models

Concise summaries of key papers in responsible AI.

This repository contains the code of a website to label the commentary for Reinforcement Learning decisions in Pong and Maze environment.

Explainable Speaker Recognition system

Code of experiments implemented in the paper "Explainability of Predictive Process Monitoring results: Techniques, Experiments and Lessons Learned", comparing XAI methods at different granularities (global/local) with different settings on predictive process monitoring outcomes using process mining event logs

Implementing text classification algorithms using the 20 newsgroups datasets, with python

In this article, the factors affecting BERT's transferability is explained through visualizations

We aim to analyze and comment on the psychological impact that the pandemic of COVID-19 has had on the world. Using Explainable AI, we extract the importance of factors contributing to stress and give an overview of what a country needs to improve on, to better handle its next pandemic.

Explainable AI for Healthcare

Official Website of https://github.com/tamlhp/awesome-privex

A scoring system for explainability

Dedicated to providing cutting-edge solutions in the field of data science, covering a range of advanced techniques like model training, evaluation etc.

Repository for "Endogenous Macrodynamics in Algorithmic Recourse" (Altmeyer et al., 2023)