This course is designed for anyone who wants to gain a deeper understanding about the importance of trust and responsibility in AI, analytics, and innovation. The content is especially geared to those who are making business decisions based on machine learning and AI systems and those who are designing and training AI systems.
Whether you are a programmer, an executive, an advisory board member, a tester, a manager, or an individual contributor, this course helps you gain foundational knowledge and skills to consider the issues related to responsible innovation and trustworthy AI. Empowered with the knowledge from this course, you can strive to find ways to design, develop, and use machine learning and AI systems more responsibly.
We expect that each module can be completed in under an hour, and you can work at your own pace to complete the material. As we release new modules, you might lose progress through the material that you have completed, so please make a note of where you are leaving off before exiting the course.
Learn How To
Explain how trustworthy AI integrates with the AI and analytics life cycle and the data supply chain.Identify unwanted biases throughout the AI and analytics life cycle. Define principles of responsible innovation. Develop a lens for the principles of responsible innovation in action. Apply the principles of human-centricity, inclusivity, accountability, privacy and security, robustness, and transparency to scenarios of responsible innovation and trustworthy AI.Identify how SAS technologies address unwanted bias and innovate responsibly in data management, model development, and model deployment.Who Should Attend
Data consumers, IT professionals, managers, analysts, data scientists, and anyone else who uses, designs, consumes information from, or makes decisions based on data and AI
Prerequisites
There are no formal prerequisites to this course, although it is helpful to have a working level of data literacy, which can be obtained in the Data Literacy Essentials course or the Data Literacy in Practice course (or both).
SAS Products Covered
None
Course Outline
Overview of Trustworthy AI and the Analytics Life Cycle
Responsible innovation.AI and analytics life cycle.Trustworthy AI: How can SAS help?Data chain of custody.AI Risk and Unintentional BiasUnintentional bias.Ethics.Unwanted bias and the data chain of custody.Principles of Responsible Innovation OverviewPrinciples of responsible innovation.Focus on Human CentricityDefining human-centricity.Evaluating human-centricity.Scenario: palliative risk score model. Scenario: firing decisions taken by bots. Scenario: police raid home of innocent person. Focus on InclusivityDefining inclusivity.Evaluating inclusivity.Scenario: fixing racial bias in EEG research.Scenario: introducing the first female crash test dummy.Scenario: racial disparities in automated speech recognition.Focus on AccountabilityDefining accountability.Evaluating accountability.Scenario: introducing the first female crash test dummy (revisited).Focus on Privacy and SecurityDefining privacy and security.Evaluating privacy and security.Scenario: mobile device encryption and security.Scenario: mental health crisis data.Focus on RobustnessDefining robustness.Evaluating robustness.Scenario: cryptocurrency exchange failure.Scenario: credit rating agencies and the subprime crisis.Discussion: why robustness is key to deploying AI.Focus on TransparencyDefining transparency.Evaluating transparency.Scenario: law school rankings.Scenario: energy app for an electric vehicle.Scenario: credit score literacy program.Resource: guiding questions.SAS Technology for Responsible Data ManagementRobust data governance.SAS Technology for Responsible Model DevelopmentTransparent AI.SAS Technology for Responsible Model DeploymentSecure Model Ops processes.