Microsoft’s framework for building AI systems responsibly

Responsible AI Maturity Model

Responsible AI Impact Assessment Guide

Responsible AI Impact Assessment Template

Assess AI systems by using the Responsible AI dashboard

Getting started with Azure Machine Learning Responsible AI components (Part 1)

RAI Dashboard Supported scenarios and limitations

Responsible AI Accelerator

RAI for Azure AutoML

Responsible AI: The research collaboration behind new open-source tools offered by Microsoft

Responsible AI Toolbox

Responsible AI with LLMs

Configure content filters with Azure OpenAI Service

Video - Azure Responsible AI Dashboard

1. From your Azure Machine Learning(AML) compute instance, start a Jupyter Session, then open a terminal session from the Upper right drop down menu "New"

   

Clone the following github repo

git clone https://github.com/sqlshep/RAI-Hack.git

2. Navigate to the newly cloned folder in Jupyter or VS Code and select the folder of the project you would like to work on,"Diabetes_Story", "Education_Story", "Finance_Story" or "Healthcare_Story". Then select the .ipynb notebook in that folder and start working.

   Keep in mind that each notebook takes about 30 minutes in total to run. While it is running you can monitor the model training and RAI dashboard creation phase in the AML portal under Pipelines, there will be two pipelines created.

3. Once complete, Navigate to Models in teh AML Portal and select the most recent model created;

   

For a deep Dive into the RAI Dashboard Getting started with Azure Machine Learning Responsible AI components