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xplique | DiCE | |
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1 | 2 | |
572 | 1,276 | |
3.5% | 1.4% | |
8.4 | 8.2 | |
8 days ago | 21 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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xplique
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?
OmniXAI - OmniXAI: A Library for eXplainable AI
CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
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
phoenix-chat-example - 💬 The Step-by-Step Beginners Tutorial for Building, Testing & Deploying a Chat app in Phoenix 1.7 [Latest] 🚀
kaisuu - Japan's Kanji Usage on Twitter in Realtime
TalkToModel - TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!