Responsible Innovation and Trustworthy AI

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SAS | Curiosity

Responsible Innovation and Trustworthy AI

Lernangebot wird durch externen Anbieter bereitgestellt

Responsible Innovation and Trustworthy AI

Künstliche Intelligenz

7 Stunden



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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.

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.

Was wirst du lernen?

  • 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.

Welche Vorkenntnisse brauchst du?

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).