AI Literacy: Responsible, Trustworthy, Effective

SAS via Coursera Specialization

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This specialization is designed for anyone who wants to gain a deeper understanding about the importance of trust and responsibility in AI, analytics, and innovation. It is also for learners who want to understand different types of Generative AI and see examples of how SAS can enhance your efforts to make the most of these techniques. The courses are especially geared to those who are making business decisions based on AI systems and outputs of generative AI, as well as those who are designing and training AI systems.

Syllabus

Course 1: Responsible Innovation and Trustworthy AI
Course 2: Ethical Use of AI Agents and Agentic AI
Course 3: Generative AI Using SAS

Courses

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4 hours 37 minutes
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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
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5 hours 2 minutes
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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
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9 hours 55 minutes
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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. Learn How To: 1. Explain how trustworthy AI integrates with the AI and analytics life cycle and the data supply chain. 2. Identify unwanted biases throughout the AI and analytics life cycle. 3. Define principles of responsible innovation. 4. Develop a lens for the principles of responsible innovation in action. 5. Apply the principles of human-centricity, inclusivity, accountability, privacy and security, robustness, and transparency to scenarios of responsible innovation and trustworthy AI. 6. 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).