DiCE
kaisuu
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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.
kaisuu
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Tracking mentions began in Dec 2020.
What are some alternatives?
OmniXAI - OmniXAI: A Library for eXplainable AI
magnetissimo - Web application that indexes all popular torrent sites, and saves it to the local database.
CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
ex_chain - Simple Markov Chain written in Elixir
AIX360 - Interpretability and explainability of data and machine learning models
elixir_koans - Elixir learning exercises
interpret - Fit interpretable models. Explain blackbox machine learning.
dice - Roll the dice, in elixir
harakiri - Help applications kill themselves
feedx - Generic feed adding social features to current applications.
stranger - Chat anonymously with a randomly chosen stranger
rubix - A very simple (and barely-functioning) Ruby runner for Elixir