Building Trustworthy AI Systems: Transparency, Explainability, and Control with ISO/IEC TR 24028 .MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 17m | 145 MB
Instructor: Lyron Andrews
As the global regulatory and legal environments work to catch up to the rapidly changing and evolving world of AI, many mandates have started to require proof of trustworthiness related to developing and deploying AI systems. In this course, instructor Lyron Andrews outlines how you can use the guidance from the ISO/IEC TR 24028 standard to leverage AI tools according to principles of trustworthiness, which include explainability, transparency, and controllability.
Learning objectives - Analyze approaches to establishing trust in AI systems through transparency, explainability, and controllability.
- Evaluate engineering pitfalls, threats, and risks associated with AI systems, along with potential mitigation techniques.
- Apply methods to assess and improve the availability, resiliency, reliability, accuracy, safety, security, and privacy of AI systems.
- Explain the concepts of trustworthiness in AI, including explainability, transparency, and controllability, in the context of evolving global regulatory and legal environments.
- Design AI systems with careful consideration of explainability, transparency, and controllability to enhance their trustworthiness.
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