ContrXT
DiCE
ContrXT | DiCE | |
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1 | 2 | |
24 | 1,276 | |
- | 1.4% | |
0.0 | 8.2 | |
almost 2 years ago | 20 days ago | |
Python | Python | |
GNU General Public License v3.0 only | MIT License |
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ContrXT
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?
TalkToModel - TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
OmniXAI - OmniXAI: A Library for eXplainable AI
DALEX - moDel Agnostic Language for Exploration and eXplanation
CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
nlp-recipes - Natural Language Processing Best Practices & Examples
AIX360 - Interpretability and explainability of data and machine learning models
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
interpret - Fit interpretable models. Explain blackbox machine learning.
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
harakiri - Help applications kill themselves
refinery - The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
stranger - Chat anonymously with a randomly chosen stranger