A curated list of awesome responsible machine learning resources.

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

A Python package to assess and improve fairness of machine learning models.

推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

moDel Agnostic Language for Exploration and eXplanation

Gno: An interpreted, stack-based Go virtual machine to build succinct and composable apps + gno.land: a blockchain for timeless code and fair open-source.

A Go library for serving resources fairly

Bias Auditing & Fair ML Toolkit

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

An experimental platform for federated learning.

H2O.ai Machine Learning Interpretability Resources

A curated list of trustworthy deep learning papers. Daily updating...

A curated list of awesome Fairness in AI resources

Conformalized Quantile Regression

Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

Python code for training fair logistic regression classifiers.

The LinkedIn Fairness Toolkit (LiFT) is a Scala/Spark library that enables the measurement of fairness in large scale machine learning workflows.

ST-SSL (STSSL): Spatio-Temporal Self-Supervised Learning for Traffic Flow Forecasting/Prediction