jennifermarsman/ResponsibleAI-Airlift

Given the importance of responsible development and deployment of AI systems, the goal of this session is to equip you with the knowledge you need to successfully use, apply, and promote these offerings on customer use cases. Please forward this invite to other solution architects, evangelists, field activators, etc. We truly hope we can limit this workshop to those of you who work directly with customers on ML and AI topics.

ResponsibleAI-Airlift-July2020

Overview

This repository contains content of a two-part workshop for using machine learning interpretability and fairness assessment (+ unfairness mitigation) to build fairer and more transparent models. The different components of the workshop are as follows:

• Part 1: Interpretability with glassbox models (EBM)
• Part 2: Explain blackbox models with SHAP (and upload explanations to Azure Machine Learning)
• Part 3: Run Interpretability on Azure Machine Learning
• Part 4: Model fairness assessment and unfairness mitigation

Getting started with the workshop environment

1. Provision your personal Lab environment

   • Open the Registration URL: https://bit.ly/2OreKYy
   • Enter the Activation Code which should be provided by the instructors of the workshop.
   • Fill out the registration form and Submit it.
   • On the next screen click Launch Lab.
   • Wait until your personal environment is provisioned. It should take approximately 3-5 minutes.

2. Login to Azure ML studio

   • Once the workshop enviroment is ready, you can open new browser tab and navigate to Azure ML studio, using its direct URL: https://ml.azure.com. We recommend using a Private Browser window for the login, to avoid conflicting credentials if you already have an Azure subscription.
   • Use the credentials provided in the workshop environment to sign-in to Azure ML studio.
   • In the Welcome screen select the preprovisioned subcription and workspace similar to screenshot below:
   • Click Get started!
   • In the welcome screen click on Take a quick tour button to familiarize yourself with Azure ML studio.

3. Create a VM for running the notebooks

   • Click on Compute tab on the left navigation bar.
   • In the "Compute Instances" section, click New.
   • Enter VM name of your choice and click Create. Creation should take approximately 5 minutes.

4. Clone this repository to Notebook VM in your Azure ML workspace

   • Once Notebook VM is created and in Running state, click on the Jupyter link. This will open Jupyter web UI in new browser tab.
   • In Jupyter UI click New > Terminal.
   • In terminal window, type and execute command: ls
   • Notice the name of your user folder and use that name to execute next command: cd <userfolder>
   • Clone the repository of this workshop by executing following command: git clone https://github.com/microsoft/responsibleai-airlift.git

5. Open Part 1 of the workshop

   • Go back to the Jupyter window.
   • Open Interpretability with glassbox models (EBM) notebook.

You are ready to start your workshop! Have fun.

Useful Links

Interpretability

• InterpretML open source
• InterpretML AI Show (video)
• Interpretability in AzureML, concept
• Interpretability in AzureML, how-to
• InterpretML case study

Fairness

• Fairlearn open source
• Fairlearn whitepaper
• Fairlearn AI Show (video)
• Fairness Assessment in AzureML, concept
• Fairness Assessment in AzureML, how-to
• Fairlearn case study