@responsible-ai/fairlearn

Responsible AI Core

This project provides responsible AI user interfaces for Fairlearn and interpret-community, as well as foundational building blocks that they rely on.

These include

• a shared service layer which also maintains utilities to determine the environment that it is running in so that it can configure the local flask service accordingly.
• a base typescript library with common controls used across responsible AI dashboards

Contributing

Contributor license agreement

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

If you have previously committed changes that were not signed follow these steps to sign them retroactively after setting up your GPG key as described in the GitHub documentation.

Code of conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [e-mail] with any additional questions or comments.

Development process

For all further steps yarn install is a prerequisite.

To run the dashboards locally run the following from the root of the repository on your machine:

yarn start

which can take a few seconds before printing out

$ nx serve

> nx run dashboard:serve
**
Web Development Server is listening at http://localhost:4200/
**

at which point you can follow the link to your browser and select the dashboard of your choice.

To check for linting issues and auto-apply fixes where possible run

yarn lintfix

To build a specific app run

yarn build <app-name>  // e.g. fairlearn, interpret

or alternatively yarn buildall to build all of them. Since most apps have dependencies on mlchartlib it makes sense to run yarn buildall at least once.

Debugging

There are several different ways to debug the dashboards:

1. Use Chrome + React Developer Tools. The debugging experience can be a bit flaky at times, but when it works it allows you to set breakpoints and check all variables at runtime.

2. Adding console.log(...) statements and check the console during execution. Please remember to remove the statements later on.

3. Alternatively, you can set objects as part of window to inspect them through the console at runtime (as opposed to having to specify what to print with console.log at compile time).