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fairness
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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
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...
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
LangFair is a Python library for conducting use-case level LLM bias and fairness assessments
ST-SSL (STSSL): Spatio-Temporal Self-Supervised Learning for Traffic Flow Forecasting/Prediction