Mastering Responsible AI: From Concept to Auditing
Offered By: LinkedIn Learning
Course Description
Overview
The expansion of AI highlights the need to build and deploy AI systems and products responsibly. This means pulling in cross-functional teams earlier and more often, knowing frameworks for building responsible products, and understanding algorithmic auditing to mitigate bias. This learning path provides practical information about responsible AI, governance, and algorithmic auditing to build AI products as responsibly and equitably as possible.
- Explore the concepts of algothmic bias and unfairness.
- Learn to mitigate the risks of AI while maximizing benefits.
- Use generative AI tools to mitigate algorithmic bias.
- Build algorithmic auditing/continuous monitoring processes.
Syllabus
Courses under this program:
Course 1: Foundations of Responsible AI
-Learn about the practices needed to perform fairness testing and implement responsible AI systems.
Course 2: Responsible AI: Principles and Practical Applications
-Learn how AI is being used today and how to ensure its responsible usage into the future.
Course 3: Introduction to AI Governance
-Explore core concepts and practical strategies to effectively implement and manage AI governance.
Course 4: Compliance and Regulations for Generative AI
-Learn about compliance building blocks and considerations that you should keep in mind when adopting generative AI.
Course 5: Building a Responsible AI Program: Context, Culture, Content, and Commitment
-Organizations who use AI need to ensure they do so in a socially responsible way. Learn how in this course.
Course 6: Introduction to Auditing AI Systems
-Learn the fundamentals of how to adapt to and comply with AI regulation in this introductory course on auditing AI systems for bias and discrimination.
Course 7: Algorithmic Auditing and Continuous Monitoring
-Learn responsible AI governance, including how to establish a robust algorithmic auditing and continuous monitoring process.
Course 1: Foundations of Responsible AI
-Learn about the practices needed to perform fairness testing and implement responsible AI systems.
Course 2: Responsible AI: Principles and Practical Applications
-Learn how AI is being used today and how to ensure its responsible usage into the future.
Course 3: Introduction to AI Governance
-Explore core concepts and practical strategies to effectively implement and manage AI governance.
Course 4: Compliance and Regulations for Generative AI
-Learn about compliance building blocks and considerations that you should keep in mind when adopting generative AI.
Course 5: Building a Responsible AI Program: Context, Culture, Content, and Commitment
-Organizations who use AI need to ensure they do so in a socially responsible way. Learn how in this course.
Course 6: Introduction to Auditing AI Systems
-Learn the fundamentals of how to adapt to and comply with AI regulation in this introductory course on auditing AI systems for bias and discrimination.
Course 7: Algorithmic Auditing and Continuous Monitoring
-Learn responsible AI governance, including how to establish a robust algorithmic auditing and continuous monitoring process.
Courses
-
Learn about the practices needed to perform fairness testing and implement responsible AI systems.
-
Explore core concepts and practical strategies to effectively implement and manage AI governance.
-
Learn how AI is being used today and how to ensure its responsible usage into the future.
-
Organizations who use AI need to ensure they do so in a socially responsible way. Learn how in this course.
-
Learn the fundamentals of how to adapt to and comply with AI regulation in this introductory course on auditing AI systems for bias and discrimination.
-
Learn about compliance building blocks and considerations that you should keep in mind when adopting generative AI.
-
Learn responsible AI governance, including how to establish a robust algorithmic auditing and continuous monitoring process.
Taught by
Ayodele Odubela, Jill Finlayson, Vidhi Chugh, Adrian Gonzalez Sanchez, Katrina Ingram and Brandie Nonnecke, PhD
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