#计算机科学#🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
#计算机科学#Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
#计算机科学#H2O.ai Machine Learning Interpretability Resources
Data from the largest and longest measurement of online tracking.
#计算机科学#Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms...
Mapeamento de iniciativas (e catálogos) de dados abertos governamentais no Brasil.
A library that implements fairness-aware machine learning algorithms
#计算机科学#Self-Explaining Neural Networks
#计算机科学#Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
#计算机科学#Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
#自然语言处理#An eXplainable AI system to elucidate short-term speed forecasts in traffic networks obtained by Spatio-Temporal Graph Neural Networks.
#计算机科学#Overview of machine learning interpretation techniques and their implementations
#计算机科学#The official repository for MedAI 2021 - a machine learning challenge organized by NORA and NMI
Documentation and tools for the API of opensupplyhub.org
Exploring the limits of social media transparency data
Exploring the limits of social media transparency data
An open and participatory framework to re-define how organizations assess their alignment with Decentralized Autonomous Organizations (DAOs).