SAS Trustworthy AI Life Cycle is Now Open Source
Last year, SAS released the Trustworthy AI Life Cycle Workflow for use with SAS® Model Manager and SAS® Workflow Manager. We designed this workflow to work within SAS Viya and enforce the National Institute of Standards and Technology’s (NIST) AI Risk Management Framework, a robust tool for facilitating more transparent and inclusive analyses of AI systems.
Since then, we’ve been working to improve the SAS Trustworthy AI Life Cycle—to make it not only more accessible but also easier to use. In collaboration with our Open Source Program Office and Data Ethics Practice, we launched a new, more flexible iteration of the tool, one that is platform agnostic and better suited to collaborative practice. By following its instructions, teams can review, document, and audit their analytics projects using any platform.
And today, we’re pleased to announce that version 1.0 of the SAS Trustworthy AI Life Cycle is now available on GitHub. In this article, I’ll dive into the purpose of the GitHub project, demonstrate how teams can get started using it, and explain how you can contribute to a more responsible AI landscape.
What is the SAS Trustworthy AI Life Cycle GitHub project?
The SAS Trustworthy AI Life Cycle outlines steps for evaluating and deploying a more trustworthy AI system. It aims to make the U.S. National Institute of Standards and Technology's (NIST) recommendations, standards, and best practices for AI risk management easier to adopt and follow.
The life cycle helps organizational stakeholders specify individual roles and expectations, gather required documentation, and outline factors for consideration. As a result, teams can produce documentation to support the assertion that the organization has done its due diligence to provide evidence that the model is fair and their processes do not cause harm.
The SAS Trustworthy AI Lifecycle splits the AI and analytics lifecycle into eight phases:
- Identify Stakeholders
- Document Project
- Prepare and Assess Data
- Train and Assess Models
- Test Models
- Deploy Model
- Monitor Model
- Review Project
To make the Life Cycle easier to use, SAS created a GitHub project that leverages GitHub Pages to transform a previously static document into a more dynamic site. Teams using the SAS Trustworthy AI Life Cycle start with the first step in section one, recording their responses to the tool’s questions and prompts and moving between steps when instructions direct them. The result is a living and auditable document that records a team’s careful thinking and planning as it works toward implementing responsible and trustworthy AI best practices.
To see the Life Cycle in action, check out this demo video:
What are the benefits of using this project?
By following the steps in this project, your team will:
- Have a clear understanding of roles and responsibilities.
- Create an AI model leveraging best practices and aligned with recommendations from NIST.
- Document decisions and key pieces of information in a centralized location.
- Track changes made to the documentation by each stakeholder and at each time point.
- View document history.
- Have a single source of truth for each analytical project.
- Share or store the documentation via a link.
How can I use the Trustworthy AI Life Cycle?
Creating collaborative and versioned model documentation has never been easier! First, ensure that all of your stakeholders have an account on GitHub. Next, open the sas-trustworthy-ai-life-cycle project in GitHub. From the upper-right corner, click Fork.
In the following screen, you can rename the repository if you like. Ensure that you uncheck Copy the main branch only before clicking Create Fork.
You have now created a new repository, which will open automatically. SAS has included a workflow to automatically build and deploy the website based on the documentation. This makes the documentation easy to share with stakeholders or linked within analytical projects. To enable that workflow, click the Actions tab and then click I understand workflows, go ahead and enable them.
To help keep your dependencies secure and up-to-date, next click the Settings tab, then select Code security from the options on the left. Click Enable for Dependabot version updates.
To enable GitHub pages, click Pages while in the Settings tab. Select Deploy from a branch as the source, gh-pages and root for the branch, and click Save.
You are nearly there! You need to make a few more configuration changes, but you can also use this opportunity to customize your documentation. Click on the Code tab, then go into the Website folder, open the docusaurus.config.ts file, then click the pencil icon in the upper-right corner to edit the file. Change the following properties to match the user or profile where the repository now sits:
- organizationName
- editUrl
- both instances of href
If you changed the repository name, update the following properties with the new name as well:
- projectName
- editUrl
- both instances of href
You can also change the title fields to reflect your own organization or project. Once you have made the updates, click Commit changes.
Now you are ready start! To access the first step in the lifecycle, open the docs folder. The docs folder includes the eight phases of the analytics lifecycle that will walk your team from project creation through retirement. Walk through the steps in order, unless prompted to move to another step in the flow. Click the edit button to start editing the markdown files.
When you are ready to commit your answer, click Commit Changes.
Committing your changes will automatically rebuild your documentation site. You can check the progress of your site rebuild by opening the Actions tab. When complete, your will see a URL.
Opening the URL will take you to your documentation site. This site can be linked to other documentation in your analytical project, with your team’s changes automatically updating the site while being tracked in Git.
To continue the process, keep working alongside your team to complete and document the steps in the Life Cycle.
Alternatively, if you prefer to clone the repository using the Git CLI, instead of forking it, you can download the sas-trustworthy-ai-life-cycle project as a zip file.
Then create a new project in GitHub and copy the string under the Quick setup.
From your terminal, create a folder for the repository and change to that directory. Next, run git clone followed by the string you just copied. Move all the files you downloaded earlier into the folder you just created. Finally run git add . followed by git commit and git push. Now you should see the repository from GitHub with everything in it and you are ready to make the configuration changes.
How can I contribute to the open source project?
Since the project is hosted on GitHub, potential contributors can fork the project, make their suggested changes, and add those changes to a pull request. GitHub users can also raise issues or suggest enhancements using the issues tab. Project maintainers welcome and encourage your suggestions and contributions.
How can I learn more about Responsible AI at SAS?
To learn more about Responsible AI at SAS, check out the following links:
