Responsible Innovation and Trustworthy AI

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.

This course will be released several modules at a time until all modules are available. 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.

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).

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.

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 Bias
• Unintentional bias.
• Ethics.
• Unwanted bias and the data chain of custody.
• Principles of Responsible Innovation Overview
• Principles of responsible innovation.