Fundamentals of Responsible Artificial Intelligence/ML
Fundamentals of Responsible Artificial Intelligence/ML
Designing and mantaining AI/ML models that help data subjects, are explainable, are not biased, and are compliant.
Created by Vasco Patricio | 8.5 hours on-demand video course
A RESPONSIBLE COURSE FOR RESPONSIBLE AI
Unlike other responsible AI model courses you’ll find out there, this course is comprehensive and updated. In other words, not only did I make sure that you’ll find more topics (and more in-depth) than in any other course you may find, but I also made sure to keep the information relevant to the types of models and use cases you will find nowadays. Designing responsible AI models may seem complex (and it is, to a point), but it relies on a few key, simple principles.
In this course, you’ll learn about the essentials of how models are designed without bias, how they can become explainable, and how to mitigate the ethical risks posed by them. Not only that, we’ll dive deep into the activities, stakeholders, projects and resources involved in responsible AI model design.
What you’ll learn
- Most problems with AI/ML models or their data, as well as how to address them
- How to identify and mitigate ethical risks from AI/ML models, as well as comply with regulation
- What is XAI (explainable AI), as well as the most common explanation elements and popular frameworks
- Relevant regulation that impacts AI models, and how
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