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LinkedIn Learning

Responsible AI Foundations

via LinkedIn Learning Path

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Overview

Responsible AI principles are key as AI integrates into more industries and interacts more with the public. This learning path underscores the core principles of responsible AI, frameworks to apply them practically in an enterprise organization, and tools for building fairness into AI systems.
  • Discover the key principles of Responsible AI.
  • Explore different—and responsible—applications of AI.
  • Understand the steps to design responsible algorithms.

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: Ethics in the Age of Generative AI
-Learn why ethical considerations are a critical part of the generative AI creation and deployment process and explore ways to address these ethical challenges.

Course 3: Responsible AI: Principles and Practical Applications
-Learn how AI is being used today and how to ensure its responsible usage into the future.

Course 4: AI Accountability: Build Responsible and Transparent Systems
-Learn why it's absolutely crucial for AI-related data science work to be transparent, explainable, accountable, and ethical in its design and execution.

Course 5: The State of AI and Copyright
-Gain valuable insights on the current state of AI and copyright laws in this panel discussion with UK-based attorney Matt Hervey and US-based attorney Jennifer Maisel.

Course 6: Introduction to AI Governance
-Explore core concepts and practical strategies to effectively implement and manage AI governance.

Courses

Taught by

Ayodele Odubela, Vilas Dhar, Jill Finlayson, Barton Poulson, PhD, Garrick Chow and Vidhi Chugh

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