awesome-shapley-value
AIX360
| awesome-shapley-value | AIX360 | |
|---|---|---|
| 1 | 2 | |
| 133 | 1,538 | |
| 4.5% | 2.3% | |
| 3.2 | 8.2 | |
| almost 2 years ago | 2 months ago | |
| Python | ||
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-shapley-value
AIX360
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
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[R] Explaining the Explainable AI: A 2-Stage Approach - Link to a free online lecture by the author in comments
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques https://arxiv.org/abs/1909.03012 https://github.com/Trusted-AI/AIX360
What are some alternatives?
responsible-ai-toolbox - 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.
AIF360 - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
shapley - The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
explainable-cnn - 📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
shap - A game theoretic approach to explain the output of any machine learning model. [Moved to: https://github.com/shap/shap]
cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both
DALEX - moDel Agnostic Language for Exploration and eXplanation
DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.
interpret - Fit interpretable models. Explain blackbox machine learning.
backpack - BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.
TorchDrift - Drift Detection for your PyTorch Models