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).
