explanability

(WWW'21) ATON - an Outlier Interpreation / Outlier explanation method

Codebase for "Demystifying Black-box Models with Symbolic Metamodels", NeurIPS 2019.

We introduce XBrainLab, an open-source user-friendly software, for accelerated interpretation of neural patterns from EEG data based on cutting-edge computational approach.

CAVES-dataset accepted at SIGIR'22

[TMLR] "Can You Win Everything with Lottery Ticket?" by Tianlong Chen, Zhenyu Zhang, Jun Wu, Randy Huang, Sijia Liu, Shiyu Chang, Zhangyang Wang

tornado plots for model sensitivity analysis

Comprehensible Convolutional Neural Networks via Guided Concept Learning

A solution to the Boolean Blindness problem.

MechaMap - Toolkit for Mechanistic Interpretability (MI) Research

List of papers in the area of Explainable Artificial Intelligence Year wise

TS4NLE is converts the explanation of an eXplainable AI (XAI) system into natural language utterances comprehensible by humans.

Code for ER-Test, accepted to the Findings of EMNLP 2022

A framework for evaluating auto-interp pipelines, i.e., natural language explanations of neurons.

Pipeline em Python para classificação de trechos literários em três classes operacionais de complexidade cognitiva textual: baixa, média e alta, usando PLN, métricas linguísticas e aprendizado de máquina.

An AI-driven system for detecting dark patterns in user interfaces, enhanced with explainability to clearly interpret why a pattern is flagged. Designed to improve transparency, trust, and ethical design in digital products.

The mechanisms behind image classification using a pretrained CNN model in high-dimensional spaces 🏞️

Experiments to explain entity resolution systems

ML Pipeline. Detail documentation of the project in README. Click on actions to see the script.

Code for actual causes filtering based on relevance metrics.

Machine learning system that detects payment fraud and explains each decision, with proper handling of class imbalance and data bias.