explainable-ai

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

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

Graph-Native Infrastructure for Context and Accountable AI Systems

Fit interpretable models. Explain blackbox machine learning.

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

High-Performance Symbolic Regression in Python and Julia

Debugging, monitoring and visualization for Python Machine Learning and Data Science

The context orchestration layer powered by hypergraphs. Build a unified semantic context layer where agentic outcomes are deterministic and agent behavior is not just traceable, but cryptographically verifiable.

The Self-Coding System for Your App — Alan AI SDK for Web

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

AI on the way. An auto deep learning pipe dream. An RDBMS approach to deep learning. Declarative, explainable, scalable, optimizable, easy to deploy, all that good stuff.

Interpretability and explainability of data and machine learning models

A collection of research materials on explainable AI/ML

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

Generate Diverse Counterfactual Explanations for any machine learning model.

moDel Agnostic Language for Exploration and eXplanation

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

XAI - An eXplainability toolbox for machine learning

[CONTRIBUTORS WELCOME] Generalized Additive Models in Python

OmniXAI: A Library for eXplainable AI

ICCV 2023-2025 Papers: Discover cutting-edge research from ICCV 2023-25, the leading computer vision conference. Stay updated on the latest in computer vision and deep learning, with code included. ⭐ support visual intelligence development!

Make your agents talk human. ADHD-friendly output styles for Claude Code, Codex, and others. So you can pay attention, not tokens.

Leave One Feature Out Importance

Papers about explainability of GNNs

Distributed High-Performance Symbolic Regression in Julia

Diffusion attentive attribution maps for interpreting Stable Diffusion.

Shapley Interactions and Shapley Values for Machine Learning

👋 Xplique is a Neural Networks Explainability Toolbox

Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.

[JMLR 2023] Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations