68 Open source projects that are alternatives of or similar to ml-fairness-framework

Fast approximate Shapley values in R

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

Fit interpretable models. Explain blackbox machine learning.

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

A curated list of awesome machine learning interpretability resources.

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

Predictive AI layer for existing databases.

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

Papers about explainability of GNNs

Implementation of the paper "Shapley Explanation Networks"

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Examples of Data Science projects and Artificial Intelligence use cases

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

WEFE: The Word Embeddings Fairness Evaluation Framework. WEFE is a framework that standardizes the bias measurement and mitigation in Word Embeddings models. Please feel welcome to open an issue in case you have any questions or a pull request if you want to contribute to the project!

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

Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020

Explaining the output of machine learning models with more accurately estimated Shapley values

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

GAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations

(Explainable AI) - Learning Non-Monotonic Logic Programs From Statistical Models Using High-Utility Itemset Mining

MindsDB server allows you to consume and expose MindsDB workflows, through http.

Model verification, validation, and error analysis

Official Implementation of Remembering for the Right Reasons (ICLR 2021)

Examples of unfairness detection for a classification-based credit model

Molecular Explanation Generator

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

Create interactive dashboards for your Convolutional Neural Networks with a single line of code!

An awesome & curated list for Artificial General Intelligence, an emerging inter-discipline field that combines artificial intelligence and computational cognitive sciences.

Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.

Must-read papers and resources related to causal inference and machine (deep) learning

Explains machine learning models fast using the Anchor algorithm originally proposed by marcotcr in 2018

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

Code for the ICLR 2021 paper "A Distributional Approach to Controlled Text Generation"

Data generator for Arena - interactive XAI dashboard

A library for graph deep learning research

A repository for explaining feature attributions and feature interactions in deep neural networks.

Variants of Vision Transformer and its downstream tasks

Visualize BERT's self-attention layers on text classification tasks

No description or website provided.

Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems

This Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.

Using deep learning to discover interpretable representations for mammogram classification and explanation

A Python package for causal inference using Synthetic Controls

Essential NLP & ML, short & fast pure Python code

⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.

Library containing attribution and interpretation methods for deep nets.

[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias

Visualization tool for Graph Neural Networks

This is a Pytorch implementation of GraphLIME

Tilted Empirical Risk Minimization (ICLR '21)

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.

Machine Learning in one line of code

Machine Learning Finite State Machine Models from Data with Genetic Algorithms

A Julia package for interpretable machine learning with stochastic Shapley values