May 1, 2024
Updated July 10, 2025
13 minute read
Google AI Principles is a set of guidelines that Google has developed to ensure that its artificial intelligence (AI) systems are used responsibly and ethically. These principles cover a wide range of topics, including fairness, accountability, and transparency. By adhering to these principles, Google aims to ensure that its AI systems are used to benefit society and not to cause harm.
Why Learn About Google AI Principles?
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Find a path to becoming a Google AI Principles. Learn more at:
OpenCourser.com/topic/rnmgzn/google
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Google AI Principles.
Provides a comprehensive overview of the field of artificial intelligence, covering a wide range of topics from machine learning to natural language processing.
Provides a comprehensive overview of machine learning, with a focus on the mathematical foundations.
Provides a comprehensive overview of deep learning, a subfield of artificial intelligence that has seen significant progress in recent years.
Provides a practical guide to machine learning, with a focus on helping readers develop their own machine learning models.
Provides a comprehensive overview of reinforcement learning, a subfield of artificial intelligence that focuses on teaching agents how to learn from their mistakes.
Provides a comprehensive overview of computer vision, a subfield of artificial intelligence that focuses on teaching computers how to see.
Provides a practical guide to natural language processing, a subfield of artificial intelligence that focuses on teaching computers how to understand and generate human language.
Provides a comprehensive overview of probabilistic graphical models, a powerful tool for representing and reasoning about uncertainty.
Provides a comprehensive overview of information theory, a mathematical framework for understanding and quantifying information.
Provides a comprehensive overview of reinforcement learning algorithms, a set of techniques for teaching agents how to learn from their mistakes.
Provides a comprehensive overview of convex optimization, a mathematical framework for optimizing functions that are convex.
Provides a practical guide to machine learning for non-experts, with a focus on teaching readers how to build and deploy machine learning models.
Provides a practical guide to automating tasks with Python, a programming language that is popular for machine learning and artificial intelligence.
Provides a practical guide to data analysis with Python, a programming language that is popular for machine learning and artificial intelligence.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/rnmgzn/google
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