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awesome-machine-learning-interpretability

A curated list of awesome responsible machine learning resources.
https://github.com/jphall663/awesome-machine-learning-interpretability

Last synced: about 8 hours ago
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Community and Official Guidance Resources
Technical Resources
- Common or Useful Datasets
- Machine Learning Environment Management Tools
  - dvc
  - mlflow
  - gigantum - driven science." |
- Open Source/Access Responsible AI Software Packages
  - Hugging Face, BiasAware: Dataset Bias Detection
  - DrWhyAI
  - TensorBoard Projector
  - What-if Tool
  - AI Explainability 360
  - algofairness
  - Bayesian Case Model
  - Bayesian Rule List (BRL)
  - Falling Rule List (FRL)
  - Grad-CAM - CAM is a technique for making convolutional neural networks more transparent by visualizing the regions of input that are important for predictions in computer vision models. |
  - parity-fairness
  - ProtoPNet - duke?style=social) | "This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition" (to appear at NeurIPS 2019), by Chaofan Chen (Duke University), Oscar Li| (Duke University), Chaofan Tao (Duke University), Alina Jade Barnett (Duke University), Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University).” |
  - pymc3 - devs/pymc3?style=social) | "PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.” |
  - pytorch-grad-cam - grad-cam?style=social) | "a package with state of the art methods for Explainable AI for computer vision. This can be used for diagnosing model predictions, either in production or while developing models. The aim is also to serve as a benchmark of algorithms and metrics for research of new explainability methods.” |
  - rationale
  - Decision Trees - parametric supervised learning method used for classification and regression.” |
  - Generalized Linear Models
  - scikit-multiflow
  - shap
  - text_explainability - known state-of-the-art explainability approaches for text can be composed.” |
  - text_sensitivity
  - xplique - ai/xplique?style=social) | "A Python toolkit dedicated to explainability. The goal of this library is to gather the state of the art of Explainable AI to help you understand your complex neural network models.” |
  - ALEPlot - order interaction effects in black-box supervised learning models." |
  - arules
  - DALEXtra: Extension for 'DALEX' Package
  - elasticnet - Net and also provides functions for doing sparse PCA." |
  - fairness
  - forestmodel
  - fscaret
  - gam
  - glm2
  - glmnet - net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression." |
  - Penalized Generalized Linear Models
  - Monotonic GBM
  - Sparse Principal Components (GLRM)
  - ICEbox: Individual Conditional Expectation Plot Toolbox
  - ingredients
  - live
  - modelDown
  - modelOriented - based MI².AI. |
  - quantreg
  - rpart
  - RuleFit
  - Scalable Bayesian Rule Lists (SBRL)
  - shapper
  - smbinning
  - Monotonic
  - shap
  - Sparse Principal Components (GLRM)
  - cdt15, Causal Discovery Lab., Shiga University - Gaussianity of the data." |
  - Scikit-Explain - friendly Python module for machine learning explainability," featuring PD and ALE plots, LIME, SHAP, permutation importance and Friedman's H, among other methods. |
  - LDNOOBW
  - RuleFit
  - interpret: Fit Interpretable Machine Learning Models
  - Scikit-learn - learn.org/stable/modules/decomposition.html#sparse-principal-components-analysis-sparsepca-and-minibatchsparsepca) | "a variant of [principal component analysis, PCA], with the goal of extracting the set of sparse components that best reconstruct the data.” |
- Benchmarks
Miscellaneous Resources
Education Resources

Programming Languages

Jupyter Notebook 5 Python 5 HTML 3 MDX 1 CSS 1

Categories

Community and Official Guidance Resources 552 Education Resources 282 Miscellaneous Resources 201 Technical Resources 85

Sub Categories

Community Frameworks and Guidance 316 Critiques of AI 160 Official Policy, Frameworks, and Guidance 146 Comprehensive Software Examples and Tutorials 139 Conferences and Workshops 90 Glossaries and Dictionaries 77 Open Source/Access Responsible AI Software Packages 55 Open-ish Classes 34 Free-ish Books 30 Common or Useful Datasets 15 Benchmarks 12 AI Law, Policy, and Guidance Trackers 11 List of Lists 10 AI Incident Information Sharing Resources 9 Curated Bibliographies 6 Challenges and Competitions 3 Machine Learning Environment Management Tools 3 Podcasts and Channels 2 Groups and Organizations 2

Keywords

llm 4 chatgpt 3 interpretable-ai 2 deep-learning 2 llm-security 2 awesome-list 2 prompt 2 reliable-ai 2 openai 2 jailbreak 2 llms 2 ai 2 privacy 1 security 1 llm-privacy 1 infosectools 1 adversarial-machine-learning 1 security-scanner 1 vulnerability-scanner 1 large-language-model 1 prompt-engineering 1 language-model 1 responsible-ai 1 red-team-tools 1 generative-ai 1 ai-red-team 1 gpt-4 1 gpt-3-5 1 sbom 1 mlbom 1 ml 1 aibom 1 robust-machine-learning 1 deep-neural-networks 1 computer-vision 1 online-courses 1 nlp 1 natural-language-processing 1 large-language-models 1 courses 1 awesome 1 trustworthy-ai 1 neural-networks 1 xai 1 transparency 1 python 1 machine-learning-interpretability 1 machine-learning 1 lime 1 interpretable-ml 1