In this course, you will learn about responsible AI practices. First, you will be introduced to what responsible AI is. You will learn how to define responsible AI, understand the challenges that responsible AI attempt to overcome and explore the core dimensions of responsible AI.
Then, you will dive into some topics for developing responsible AI systems. You will be introduced to the services and tools that AWS offers to help you with responsible AI. You will also learn about responsible AI considerations for selecting a model and preparing data for your AI systems.
Finally, you learn about transparent and explainable models. You will gain a solid understanding for what it means for a model to be transparent and explainable. You will also explore tradeoff considerations for transparent models and the principles of human-centered design for explainable AI.
Who should attend
This course is intended for the following:
- Individuals interested in machine learning and artificial intelligence, independent of a specific job role
Course Prerequisites
Responsible AI Practices is part of a series that facilitates a foundation on artificial intelligence, machine learning, and generative AI. If you have not done so already, it is recommended that you complete these two courses:
- Fundamentals of Machine Learning and Artificial Intelligence
- Exploring Artificial Intelligence Use Cases and Applications
What you will learn
In this course, you will learn how to do the following:
- Describe responsible AI
- Explain biases in AI models
- Identify risk of generative AI
- Identify the core dimensions of responsible AI
- Describe services and tools AWS offers for responsible AI
- Explain responsible practices for selecting a model
- Describe responsible characteristics of responsible datasets
- Describe transparent and explainable models
- Identify responsible tradeoffs of AI models
- Explain the principles of human centered design
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