SAS adds to its trustworthy AI offerings with model cards and AI governance services

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Apr 17, 2024, 15:17 ET

'Nutrition labels' for models encourage transparency and surface potential bias and model drift, while supporting open source models

LAS VEGAS, April 17, 2024 /PRNewswire/ -- SAS INNOVATE -- Data and AI leader SAS is launching new trustworthy AI products and services to improve AI governance and support model trust and transparency. Model cards and new AI Governance Advisory services will help organizations navigate the turbulent AI landscape, mitigating risk and helping them pursue AI goals more confidently. SAS has also published a Trustworthy AI Life Cycle Workflow, mapped to the National Institute of Standards and Technology (NIST) AI Risk Management Framework.

Example of a SAS model card, a well-curated view on model health, reliability and performance. This view offers a “Pass/Fail” evaluation of the model’s health in terms of its status against selected metrics, as well as visual content about the most impactful variables and the sensitive or private nature of some of those variables.

"Our customers are enthusiastic about the potential of AI but remain cautious about when and how to use it," said Reggie Townsend, Vice President, SAS Data Ethics Practice. "They're asking good questions about responsible and ethical AI. Our goal is to give them the tools and guidance, based on decades of experience, to integrate AI in ways that boost profitability while reducing unintended harm."

Model cards: Trustworthy AI's 'nutrition labels'

It can be difficult to take something as complex and sophisticated as an AI model and convert it into something easily digestible for everyone involved in the AI life cycle. And as new rules and regulations are passed around the world, the ability to understand and share with regulators how a model is performing will be crucial. Model cards, an upcoming feature in SAS^® Viya^®, will serve stakeholders across the AI life cycle. From developers to board directors, all stakeholders will find value in a curated tool that supports proprietary and open source models.

Releasing mid-2024, model cards are best described as "nutrition labels" for AI models. The SAS approach is to autogenerate model cards for registered models with content directly from SAS products, removing the burden from individual users to create them. Additionally, because SAS Viya already has an existing architecture for managing open source, model cards will also be available for open source models, starting with Python models.

Model cards will highlight indicators like accuracy, fairness and model drift, which is the decay of model performance as conditions change. They include governance details like when the model was last modified, who contributed to it, and who is responsible for the model, allowing organizations to address abnormal model performance internally. The model usage section addresses intended use, out-of-scope use cases, and limitations, which will be key factors as transparency and model auditing likely become regulated business operations. Model cards were showcased earlier this year at SAS Insight, a conference for analysts.

"SAS is taking a thoughtful approach to how it helps customers embrace AI, focusing on the practical realities and challenges of deploying AI in real industry settings," said Eric Gao, Research Director at analyst firm IDC. "Model cards will be valuable for monitoring AI projects and promoting transparency."

New AI governance group led by ethical AI veteran

With the proliferation of AI, SAS customers have become increasingly concerned with how to use their data in ways that are both productive and safe. To help them on their data and AI journeys, SAS is launching AI Governance Advisory, a value-added service for current customers.

Beginning with a short meeting, SAS AI Governance Advisory will help customers think through what AI governance means in the context of their organizations. SAS has piloted this service, and customers have noted several benefits:

Increased productivity from trusted and distributed decision making.
Improved trust from better accountability in data usage.
The ability to win and keep top talent who demand responsible innovation practices.
Increased competitive advantage and market agility from being "forward compliant."
Greater brand value for confronting the potential impacts to society and the environment.

PZU Insurance of Poland is one of the largest financial institutions in central and eastern Europe. A longtime SAS customer, PZU deploys AI in areas such as claims, sales, fraud detection and customer care.

"Our AI governance conversations with SAS helped us consider potential unseen factors that could cause problems for customers and our business," said Marek Wilczewski, Managing Director of Information, Data and Analytics Management (Chief Data Officer/Chief Analytics Officer) at PZU. "We better understand the importance of having more perspectives as we embark on AI projects."

Industry veteran and ethical AI expert Steven Tiell has been hired as SAS Global Head of AI Governance. Tiell, who led Accenture's global data ethics and responsible innovation practice, is also the former Vice President of AI Strategy at DataStax.

Building on emerging government standards

Last year, the US National Institute of Standards and Technology (NIST) launched an AI Risk Management Framework. It has become a valuable tool for organizations to design and manage trustworthy and responsible AI in the absence of official regulations.

SAS has created a Trustworthy AI Life Cycle workflow, which makes NIST's recommendations easier to adopt for organizations by specifying individual roles and expectations, gathering required documentation, outlining factors for consideration, and leveraging automation to ease adoption. Organizations end up with a production model with documentation showing that they did due diligence to help ensure that the model is fair, and their processes do not cause harm.

The workflow allows organizations to document their considerations of AI systems' impacts on human lives. It includes steps to ensure that the training data is representative of the impacted population and that the model predictions and performance are similar across protected classes. These steps help ensure that the model is not causing disparate impact or harm to specific groups. Furthermore, users can ensure that a model remains accurate over time by creating human-in-the-loop tasks to act when additional attention is needed.

The SAS Trustworthy AI Life Cycle workflow can be downloaded from the SAS Model Manager Resources Github and will soon be available through the NIST AI Resource Center.

Today's announcement was made at SAS Innovate, the data and AI experience for business leaders, technical users and SAS Partners. Keep up with the latest news from SAS by following @SASsoftwareNews on X/Twitter.

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