Generating and validating natural-language explanations.
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Updated
Feb 3, 2025 - HTML
Generating and validating natural-language explanations.
Minimal starting point for rapid prototyping interactive Human-AI tools
Experiments with experimental rule-based models to go along with imodels.
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.
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
A little lab of learning algorithms (in LUA). Less XAI, but better
In this article, the factors affecting BERT's transferability is explained through visualizations
A scoring system for explainability
HTML version of the book Feature Engineering & Selection for Explainable Models A Second Course for Data Scientists
Website for health data science at KDD 2021
Show case for modelStudio based on ⚽⚽⚽FIFA 20 ⚽⚽⚽
This project poses a new methodology for assessing and improving sequential concept bottleneck models (CBMs). The research undertaken in this project builds upon the model proposed by Grange et al., of which I was one of the co-authors.
Conway's Game of Life is sequential, here high-dimensional states are projected into the two-dimensional space, and connected, furthermore, meta-data is added to create interactive 2D visualizations.
Presentation given to Drexel XAI seminar on "Interpretable Explanations of Black Boxes by Meaningful Perturbation"
Explaining Tele Assist System (TAS) workflow adaptations using LIME
This Tower of Hanoi Game Prototype is an attempt at explaining Q-learning and Reinforcement Learning Principles to People in an Intuitive and Interactive Way
Showcase for Forecasting Explainability using New Car Sales in Norway
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