AI Literacy: Responsible, Trustworthy, Effective Specialization

This course is designed for anyone who wants to gain a deeper understanding about the importance of trust and responsibility in agentic AI and AI agents. The content is especially geared to those who are making business decisions based on AI agents and agentic AI and those who are designing and training such systems.

1. Distinguish between traditional AI agents and agentic AI systems in terms of scope, autonomy, and decision-making. 2. Apply six core ethical principles to real-world predictive modeling workflows and agentic system design. 3. Evaluate ethical considerations across five industry domains using structured scenario analysis. 4. Utilize practical tools—including documentation templates, ethics checklists, and governance prompts—to design trustworthy systems. 5. Understand emerging regulatory frameworks including the EU AI Act, U.S. federal and state regulations, and global AI governance resources. 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: 1. Responsible Innovation and Trustworthy AI

Generative Artificial Intelligence (GenAI) is a rapidly developing area of machine learning, with application across business, government, and academia. In this course, you will learn about different types of GenAI and see examples of how SAS can enhance your efforts to make the most of these techniques.

Learn How To: 1. Explain what generative AI is and how it fits into the broader AI landscape. 2. Describe several types of GenAI systems. 3. Name some of the key challenges and opportunities in making a trustworthy AI system. 4. Generate synthetic data with Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Networks (GANs). 5. Explain how Large Language Models (LLMs) generate meaningful text. 6. Classify text for LLMs using Bidirectional Encoder Representations from Transformers (BERT). 7. Improve the accuracy and relevance of LLM output using Retrieval Augmented Generation (RAG). Who Should Attend: Learners who want to know more about the techniques that comprise GenAI and how to make use of them with SAS Prerequisites: Before taking this course, you should have some background in statistics and machine learning using SAS. You can gain this knowledge by taking the following courses: 1. Statistics You Need to Know for Machine Learning 2. Machine Learning Using SAS Viya