【Awesome】最全的机器学习可解释性资料（machine-learning-interpretability）- 专知

【Awesome】最全的机器学习可解释性资料（machine-learning-interpretability）

【导读】 大家知道机器学习和深度学习在近几年已经取得了极大的发展，应用范围十分广泛，对各行各业也都产生了深远的影响。但是现在从事机器学习和深度学习的研究人员研究的焦点逐渐变成可解释性。什么是可解释性？可解释性就是不仅仅要知道怎么做，同时也要知道为什么这么做会产生这样的结果，不论结果是否理想。今天小编就给大家分享一些机器学习可解释性资料

下面是一个局部的机器学习蓝图，可以从总体上帮助降低做机器学习任务的风险程度。

▌资料目录

全面的软件示例和教程（Comprehensive Software Examples and Tutorials）
可解释性或合适的增强软件包（Explainability- or Fairness-Enhancing Software Package）

Browser
Python
R

免费的书（Free Books）
其他可解释性和合适的资源和列表
论文（Review and General Papers）
教学资源（Teaching Resources）
可解释（“白盒”）或合适的建模包（Interpretable ("Whitebox") or Fair Modeling Packages）

C/C++
Python
R

▌Comprehensive Software Examples and Tutorials

Getting a Window into your Black Box Model
IML
Interpretable Machine Learning with Python
Interpreting Machine Learning Models with the iml Package
Machine Learning Explainability by Kaggle Learn
Model Interpretability with DALEX
Model Interpretation series by Dipanjan (DJ) Sarkar:

The Importance of Human Interpretable Machine Learning
Model Interpretation Strategies
Hands-on Machine Learning Model Interpretation

Partial Dependence Plots in R
Visualizing ML Models with LIME

▌Expalinability-or Fairness-Enhancing Software Packages

Browser

What-if Tool

Python

‍‍aequitas
AI Fairness 360
anchor
casme
cleverhans
ContrastiveExplanation (Foil Trees)
deeplift
deepvis
eli5
fairml
fairness
Integrated-Gradients‍
lofo-importance
L2X
lime
PDPbox
pyBreakDown
PyCEbox
shap
Skater
rationale
tensorflow/lucid
tensorflow/model-analysis
Themis
themis-ml
treeinterpreter
woe
xai

ALEPlot
breakDown
DALEX
ExplainPrediction
featureImportance
forestmodel
fscaret
ICEbox
iml
lightgbmExplainer
lime
live
mcr
pdp
shapleyR
smbinning
vip
xgboostExplainer

▌Free Books

Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models
Fairness and Machine Learning
Interpretable Machine Learning

▌Other Interpretability and Fairness Resources and Lists

8 Principles of Responsible ML
An Introduction to Machine Learning Interpretability
Awesome interpretable machine learning ;)
Awesome machine learning operations
algoaware
criticalML
Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) Scholarship
Machine Learning Ethics References
Machine Learning Interpretability Resources
MIT AI Ethics Reading Group
XAI Resources

▌Review and General Papers

A Comparative Study of Fairness-Enhancing Interventions in Machine Learning
A Survey Of Methods For Explaining Black Box Models
Challenges for Transparency
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning
On the Art and Science of Machine Learning Explanations
On the Responsibility of Technologists: A Prologue and Primer
Please Stop Explaining Black Box Models for High-Stakes Decisions
The Mythos of Model Interpretability
The Promise and Peril of Human Evaluation for Model Interpretability
Towards A Rigorous Science of Interpretable Machine Learning
Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda