Authors: Jennifer Hargrove and Ruth Baldasaro

As technology continues to evolve rapidly, ethically centered model design is becoming increasingly important. In this post, we provide a brief overview of SAS Models and describe how all SAS models are built using SAS’ core principles for responsible innovation.

The SAS Applied AI and Modeling division is focused on designing and building trustworthy, industry-focused models. Drawing on decades of SAS expertise, these models help organizations solve complex business challenges with proven approaches. Examples of business problems addressed include:

• Credit Card Fraud
• Payment Integrity for Food Assistance
• Entity Resolution
• Medication Adherence
• Supply Chain Optimization
• Industry Safety
• Document Analysis

Challenges around building ethical AI models

Currently, no single global standard exists for designing ethical AI models. When organizations fail to clearly document assumptions and risks, AI models often become opaque, difficult to explain, and hard for users to trust. Given these challenges, the SAS Model development group collaborates with industry experts, consults the SAS Ethics Committee, and applies the SAS core principles for responsible innovation when developing our models.

SAS core principles for responsible innovation

The SAS core principles for responsible innovation include human-centricity, inclusivity, accountability, privacy and security, robustness, and transparency. SAS incorporates these principles into the selection, design, and function of all SAS Models. Over the next few sections, we will highlight each core principle and provide examples of how our models take into account each of these principles during design and implementation.

Human centricity: Promote human well-being, human agency and equity

SAS puts people first by developing models that improve well-being. For example, we have built models to support worker safety in the manufacturing industry, medication adherence risk in the health care industry, and payment integrity in the public sector. Each of these models focuses on using data to improve the human condition or help ensure services reach the right people.

Additionally, SAS designs its models with end users in mind. Model pipelines allow end users to control and review the data used to train and develop models. This helps ensure the model reflects their specific population, use case, and business use case.

Lastly, SAS Models are also designed to help eliminate bias and promote equity. For instance, the medication adherence risk model incorporates SAS procedures that assess the model for bias across sensitive variables such as race and gender. By incorporating bias assessments into model design, we enable end users to examine and control for equity during model deployment.

Inclusivity: Ensure accessibility and include diverse perspectives and experiences

Inclusivity is a feature of our model development, both in the selection of models to build and in the models themselves. We include diverse stakeholder feedback throughout model development to capture diverse perspectives. By including diverse stakeholder feedback, we help ensure our models reflect real-world complexities and help prevent bias. Before beginning work on a new model, our development group collaborates with several divisions across SAS and external stakeholders to review model design. Throughout model development, we set up regular touchpoints with stakeholders to review model design and output. Additionally, SAS ensures accessible model design by internationalizing our models for use in many languages. Lastly, to ensure all reports are accessible to everyone, SAS Visual Analytics reports are designed using accessible color palettes.

Accountability: Proactively identify and mitigate adverse impacts

To help ensure accountability in all our models, we proactively identify and mitigate risks and potential biases throughout the model lifespan. Examples of ways we mitigate bias include: thoughtful data procurement, extensive model testing, and documenting appropriate model applications. All SAS Models come with extensive documentation highlighting known risks. The documentation includes clear intended-use statements to help prevent inadvertent use of the model for purposes that deviate from the original design. To help mitigate and control bias, model designers carefully select data that accurately represents trends and associations found in the target population. Furthermore, SAS Models include procedures to check for missing variables and outliers that may skew model results.

Privacy and security: Protect the use and application of an individual's data

From model development to model deployment, we focus on preserving privacy and providing model security. SAS ensures privacy when developing models by using de-identified, synthetic data, or digital twin data to develop models. To prevent external access, SAS compiles the code for all SAS Models. Licensing checks also ensure only authorized users can access them.

Beyond development, our SAS Viya models leverage the privacy and security features built into SAS Viya. This includes features like automatic detection of sensitive data and data lineage tracking. SAS has prioritized the security and compliance of SAS products, with certifications from many regulatory and compliance organizations, including FEDRAMP and NIST. Learn more about SAS certifications.

Robustness: Operate reliably and safely, while managing potential risks throughout a life cycle

The SAS Modeling group uses several approaches to ensure models perform consistently and accurately across environments. First, SAS Model pipelines use champion model analysis to identify the best-fitting model based on the end user’s data. Champion model analysis deploys several model types to identify which performs best on the end user’s data. Second, our models include model-fit statistics that give end users tools to monitor model and concept drift. Third, we run all models through extensive above-the-line and below-the-line testing before deployment. Lastly, all our models leverage our own CI/CD pipeline to update and version models with the latest security and dependency updates available from SAS.

Transparency: Openly communicate intended use, risks, and how decisions are made

Transparency ensures that all end users clearly understand the model and how it arrived at its results. To support transparency, all SAS Model documentation includes detailed information about the model. SAS includes model assumptions, data requirements, potential risks, modeling decisions, and intended use statements in all model documentation. Furthermore, all our models are designed to clearly show how decisions are made and empower users with the ability to monitor the model for concept or model drift. For example, to help monitor concept drift, model pipelines include output tables such as variable importance and Shapley values to support interpretation.

Responsible AI is a team practice

Incorporating AI ethics into our modeling process helps us build better, safer, and more trustworthy models. For SAS Model developers, responsible design means asking hard questions early, documenting decisions clearly, involving the right stakeholders, and collaborating with customers to review model performance over time.

READ MORE | Trustworthy AI at SAS

LEARN MORE | SAS Responsible Innovation and Trustworthy AI Online Course