ml-fairness-frameworkFairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)
Stars: ✭ 59 (-25.32%)
Mutual labels: explainable-ai, explainable-ml, xai, interpretable-machine-learning
InterpretFit interpretable models. Explain blackbox machine learning.
Stars: ✭ 4,352 (+5408.86%)
Mutual labels: explainable-ai, explainable-ml, xai, interpretable-machine-learning
mllpThe code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
Stars: ✭ 15 (-81.01%)
Mutual labels: explainable-ai, explainable-ml, xai, interpretable-machine-learning
diabetes use caseSample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
Stars: ✭ 22 (-72.15%)
Mutual labels: explainable-ml, xai, interpretable-machine-learning
shaprExplaining the output of machine learning models with more accurately estimated Shapley values
Stars: ✭ 95 (+20.25%)
Mutual labels: explainable-ai, explainable-ml, shapley
xai-iml-sotaInteresting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
Stars: ✭ 51 (-35.44%)
Mutual labels: explainable-ml, xai, interpretable-machine-learning
dlime experimentsIn 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).
Stars: ✭ 21 (-73.42%)
Mutual labels: explainable-ai, explainable-ml, xai
ProtoTreeProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
Stars: ✭ 47 (-40.51%)
Mutual labels: explainable-ai, explainable-ml, interpretable-machine-learning
expmrcExpMRC: Explainability Evaluation for Machine Reading Comprehension
Stars: ✭ 58 (-26.58%)
Mutual labels: explainable-ai, xai
concept-based-xaiLibrary implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
Stars: ✭ 41 (-48.1%)
Mutual labels: explainable-ai, xai
XAIatERUM2020Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020
Stars: ✭ 52 (-34.18%)
Mutual labels: explainable-ai, interpretable-machine-learning
mindsdb serverMindsDB server allows you to consume and expose MindsDB workflows, through http.
Stars: ✭ 3 (-96.2%)
Mutual labels: explainable-ai, xai
MindsdbPredictive AI layer for existing databases.
Stars: ✭ 4,199 (+5215.19%)
Mutual labels: explainable-ai, explainable-ml
TensorwatchDebugging, monitoring and visualization for Python Machine Learning and Data Science
Stars: ✭ 3,191 (+3939.24%)
Mutual labels: explainable-ai, explainable-ml
responsible-ai-toolboxThis project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
Stars: ✭ 615 (+678.48%)
Mutual labels: explainable-ai, explainable-ml
zennitZennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
Stars: ✭ 57 (-27.85%)
Mutual labels: explainable-ai, xai
ShapML.jlA Julia package for interpretable machine learning with stochastic Shapley values
Stars: ✭ 63 (-20.25%)
Mutual labels: shapley, interpretable-machine-learning
