ExplainX is an explainable AI framework for data scientists to explain any black-box model behavior to business stakeholders.
interpretability explainx video code library

ExplainX.ai is a fast, scalable and end-to-end Explainable AI framework for data scientists & machine learning engineers. With explainX, you can understand overall model behavior, get the reasoning behind model predictions, remove biases and create convincing explanations for your business stakeholders.

Essential for:

  • Model debugging - Why did my model make a mistake? How can I improve the accuracy of the model?
  • Detecting fairness issues - Is my model biased? If yes, where?
  • Human-AI cooperation - How can I understand and trust the model's decisions?
  • Regulatory compliance - Does my model satisfy legal & regulatory requirements?
  • High-risk applications - Healthcare, Financial Services, FinTech, Judicial, Security etc.

Don't forget to tag @explainX in your comment, otherwise they may not be notified.

Authors community post
Share this project
Similar projects
Language Interpretability Tool (LIT)
The Language Interpretability Tool (LIT) is a visual, interactive model-understanding tool for NLP models.
Lda2vec: Tools for interpreting natural language
The lda2vec model tries to mix the best parts of word2vec and LDA into a single framework.
SuperGlue: Learning Feature Matching with Graph Neural Networks
SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.
SHAP: SHapley Additive exPlanations
A game theoretic approach to explain the output of any machine learning model.
Top collections