XAI
DiCE
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XAI
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My failure of using Python DEAP package.
An example of custom functions can be seen at: https://github.com/ben-ix/XAI/blob/master/src/base.py
DiCE
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[D] Have researchers given up on traditional machine learning methods?
- all domains requiring high interpretability absolutely ignore deep learning at all, and put all their research into traditional ML; see e.g. counterfactual examples, important interpretability methods in finance, or rule-based learning, important in medical or law applications
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[R] The Shapley Value in Machine Learning
Counter-factual and recourse-based explanations are alternative approach to model explanations. I used to work in a large financial institution, and we were researching whether counter-factual explanation methods would lead to better reason codes for adverse action notices.
What are some alternatives?
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
OmniXAI - OmniXAI: A Library for eXplainable AI
TalkToModel - TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
pytorch-lattice - A PyTorch implementation of constrained optimization and modeling techniques
AIX360 - Interpretability and explainability of data and machine learning models
interpret - Fit interpretable models. Explain blackbox machine learning.
harakiri - Help applications kill themselves
stranger - Chat anonymously with a randomly chosen stranger
shapley - The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
neuro-symbolic-sudoku-solver - ⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.
MindsDB - The platform for customizing AI from enterprise data