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Google Cloud Training
Avec l'essor de l'utilisation de l'intelligence artificielle et du machine learning en entreprise, il est de plus en plus important de développer ces technologies de manière responsable. Pour beaucoup, le véritable défi réside dans la mise en pratique de l'IA responsable, qui s'avère bien plus complexe que dans la théorie. Si vous souhaitez découvrir comment opérationnaliser l'IA responsable dans votre organisation, ce cours est fait pour vous.
Read more
Avec l'essor de l'utilisation de l'intelligence artificielle et du machine learning en entreprise, il est de plus en plus important de développer ces technologies de manière responsable. Pour beaucoup, le véritable défi réside dans la mise en pratique de l'IA responsable, qui s'avère bien plus complexe que dans la théorie. Si vous souhaitez découvrir comment opérationnaliser l'IA responsable dans votre organisation, ce cours est fait pour vous.
Read more
Avec l'essor de l'utilisation de l'intelligence artificielle et du machine learning en entreprise, il est de plus en plus important de développer ces technologies de manière responsable. Pour beaucoup, le véritable défi réside dans la mise en pratique de l'IA responsable, qui s'avère bien plus complexe que dans la théorie. Si vous souhaitez découvrir comment opérationnaliser l'IA responsable dans votre organisation, ce cours est fait pour vous.
Dans ce cours, vous allez apprendre comment Google Cloud procède actuellement, en s'appuyant sur des bonnes pratiques et les enseignements tirés, afin de vous fournir un framework pour élaborer votre propre approche d'IA responsable.
Register for this course and see more details by visiting:
OpenCourser.com/course/449shb/responsible
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What's inside
Syllabus
Introduction
Dans ce module, vous découvrirez l'impact de la technologie d'IA, l'approche de Google pour une IA responsable, ainsi que les principes de Google concernant l'IA.
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Syllabus
Introduction
Dans ce module, vous découvrirez l'impact de la technologie d'IA, l'approche de Google pour une IA responsable, ainsi que les principes de Google concernant l'IA.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Covers the ethical considerations of AI and ML, which is essential for anyone working in the field
Provides real-world examples and case studies to illustrate the application of responsible AI principles
Focuses on the operationalization of AI, which is critical for organizations looking to implement responsible AI practices
Led by Google Cloud Training, which has a wealth of expertise in responsible AI and cloud computing
May require some prior knowledge of AI and ML concepts
Taught entirely online, which may not be suitable for all learners
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Reviews summary
Practical framework for responsible ai principles
According to learners, this course offers a practical framework for operationalizing Responsible AI, particularly by applying Google Cloud principles. Students describe it as a valuable resource for understanding the ethical considerations of AI and how to implement these concepts in a business context. The course is noted for its clear structure, bridging the gap between theory and real-world application, and provides a comprehensive approach to responsible AI development.
Focuses on frameworks and principles, less on technical implementation.
"While excellent for understanding principles, I found it less focused on coding specific ethical AI solutions."
"Learners seeking very deep technical details on AI ethics might find this course more high-level than anticipated."
"It delivers well on operationalization and principles, but not on detailed ethical AI engineering practices."
Well-organized content that is easy to follow and understand.
"The course modules were logically structured, making complex topics digestible and easy to follow."
"I found the explanations clear and concise, even for someone relatively new to the operational aspects of AI ethics."
"The flow of information from AI impact to principles and then to operationalization was excellent."
Integrates Google Cloud's established responsible AI practices.
"Learning Google's approach to AI principles provided a solid, real-world example of how to implement them."
"The insights into how Google Cloud operationalizes AI ethics were particularly illuminating for me."
"It was great to see a large company's practical experience laid out in the course, providing valuable context."
Provides a clear understanding of AI ethics and principles.
"The module on ethical problems helped me critically think about potential harms and benefits of AI."
"I appreciated the deep dive into how responsible AI principles are developed and their ethical objectives."
"It offers a comprehensive overview of the ethical landscape of AI, which I find crucial today."
Focuses on operationalizing AI ethics in real-world scenarios.
"I found this course very useful for understanding how to apply responsible AI principles in my organization."
"It truly helped me bridge the gap from theoretical ethics to practical implementation within a business context."
"This course isn't just about what responsible AI is, but how to actually do it, which was exactly what I needed for my role."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Responsible AI: Applying AI Principles with GC - Français with these
activities:
Review the principles of ethical AI development
Show steps
Refreshing your knowledge of the principles of ethical AI development will help you prepare for the course and engage with the material more effectively.
Browse courses on
AI Ethics
Show steps
-
Review course materials and resources on AI ethics.
-
Attend workshops or webinars on ethical AI practices.
-
Engage in discussions or online forums on AI ethics.
Write a blog post on a specific ethical issue in AI
Show steps
Writing a blog post on an ethical issue in AI will help you consolidate your understanding and articulate your thoughts on a specific aspect of AI ethics.
Browse courses on
AI Ethics
Show steps
-
Choose a specific ethical issue related to AI that you want to focus on.
-
Research and gather relevant information on the topic.
-
Develop an outline and write your blog post.
-
Proofread and publish your blog post on a relevant platform.
Develop a business case for AI ethics
Show steps
Creating a business case for AI ethics will allow you to demonstrate the potential benefits and value of ethical AI practices within an organizational context.
Browse courses on
AI Ethics
Show steps
-
Research and gather data on the impact of AI on organizations.
-
Identify and analyze specific ethical concerns related to AI use.
-
Develop a framework for assessing and mitigating ethical risks in AI projects.
-
Create a presentation or report that outlines your findings and recommendations.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice applying ethical principles to AI scenarios
Show steps
Practicing applying ethical principles to AI scenarios will enhance your ability to identify and address ethical dilemmas in AI development and implementation.
Browse courses on
AI Ethics
Show steps
-
Identify different ethical principles and their relevance to AI.
-
Review case studies or hypothetical scenarios involving AI ethics.
-
Analyze the scenarios and apply the ethical principles to make informed decisions.
Develop an AI ethics policy framework for an organization
Show steps
By creating an AI ethics policy framework, you will demonstrate a deep understanding of ethical considerations in AI and how to develop practical guidelines for organizations.
Browse courses on
AI Governance
Show steps
-
Review existing AI ethics frameworks and guidelines.
-
Identify the key ethical principles and values relevant to the organization.
-
Develop a set of policies and procedures that address ethical concerns and risks.
-
Create a document that outlines the AI ethics policy framework and its implementation plan.
Contribute to an open-source project related to AI ethics
Show steps
Contributing to an open-source project related to AI ethics will allow you to engage with the community, learn from others, and gain practical experience in applying ethical principles to AI development.
Browse courses on
AI Ethics
Show steps
-
Research and identify open-source projects focused on AI ethics.
-
Review the project's documentation and contribute to discussions or issue tracking.
-
Propose and develop code or documentation improvements related to AI ethics.
Review the principles of ethical AI development
Show steps
Refreshing your knowledge of the principles of ethical AI development will help you prepare for the course and engage with the material more effectively.
Browse courses on
AI Ethics
Show steps
Show steps
Show steps
- Review course materials and resources on AI ethics.
- Attend workshops or webinars on ethical AI practices.
- Engage in discussions or online forums on AI ethics.
Write a blog post on a specific ethical issue in AI
Show steps
Writing a blog post on an ethical issue in AI will help you consolidate your understanding and articulate your thoughts on a specific aspect of AI ethics.
Browse courses on
AI Ethics
Show steps
Show steps
Show steps
- Choose a specific ethical issue related to AI that you want to focus on.
- Research and gather relevant information on the topic.
- Develop an outline and write your blog post.
- Proofread and publish your blog post on a relevant platform.
Develop a business case for AI ethics
Show steps
Creating a business case for AI ethics will allow you to demonstrate the potential benefits and value of ethical AI practices within an organizational context.
Browse courses on
AI Ethics
Show steps
Show steps
Show steps
- Research and gather data on the impact of AI on organizations.
- Identify and analyze specific ethical concerns related to AI use.
- Develop a framework for assessing and mitigating ethical risks in AI projects.
- Create a presentation or report that outlines your findings and recommendations.
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice applying ethical principles to AI scenarios
Show steps
Practicing applying ethical principles to AI scenarios will enhance your ability to identify and address ethical dilemmas in AI development and implementation.
Browse courses on
AI Ethics
Show steps
Show steps
Show steps
- Identify different ethical principles and their relevance to AI.
- Review case studies or hypothetical scenarios involving AI ethics.
- Analyze the scenarios and apply the ethical principles to make informed decisions.
Develop an AI ethics policy framework for an organization
Show steps
By creating an AI ethics policy framework, you will demonstrate a deep understanding of ethical considerations in AI and how to develop practical guidelines for organizations.
Browse courses on
AI Governance
Show steps
Show steps
Show steps
- Review existing AI ethics frameworks and guidelines.
- Identify the key ethical principles and values relevant to the organization.
- Develop a set of policies and procedures that address ethical concerns and risks.
- Create a document that outlines the AI ethics policy framework and its implementation plan.
Contribute to an open-source project related to AI ethics
Show steps
Contributing to an open-source project related to AI ethics will allow you to engage with the community, learn from others, and gain practical experience in applying ethical principles to AI development.
Browse courses on
AI Ethics
Show steps
Show steps
Show steps
- Research and identify open-source projects focused on AI ethics.
- Review the project's documentation and contribute to discussions or issue tracking.
- Propose and develop code or documentation improvements related to AI ethics.
Career center
Learners who complete Responsible AI: Applying AI Principles with GC - Français will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists help build artificial intelligence (AI) models. They apply mathematical and statistical techniques to data to build and maintain data models that can be used to solve business problems. This course can help prepare you for a career as a Data Scientist by teaching you the principles of AI and machine learning. You will also learn how to build and deploy AI models on the Google Cloud Platform.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and deploy machine learning models. They work with data scientists and other engineers to build and maintain machine learning systems. Responsible AI: Applying AI Principles with Google Cloud can help you prepare for a career as a Machine Learning Engineer by teaching you the principles of AI and machine learning. You will also learn how to build and deploy AI models on the Google Cloud Platform.
AI Engineer
AI Engineer
AI Engineers design, develop, and deploy AI models. They work with data scientists and other engineers to build and maintain AI systems. Responsible AI: Applying AI Principles with Google Cloud can help you prepare for a career as an AI Engineer by teaching you the principles of AI and machine learning. You will also learn how to build and deploy AI models on the Google Cloud Platform.
Software Engineer
Software Engineer
Software Engineers design, develop, and deploy software applications. They work with other engineers and business stakeholders to build and maintain software systems. Responsible AI: Applying AI Principles with Google Cloud can help you prepare for a career as a Software Engineer by teaching you the principles of AI and machine learning. You will also learn how to build and deploy AI models on the Google Cloud Platform.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. They work with data scientists and other analysts to build and maintain data analysis systems. Responsible AI: Applying AI Principles with Google Cloud can help you prepare for a career as a Data Analyst by teaching you the principles of AI and machine learning. You will also learn how to build and deploy AI models on the Google Cloud Platform.
Product Manager
Product Manager
Product Managers develop and manage products. They work with engineers, designers, and other stakeholders to build and maintain products that meet the needs of customers. Responsible AI: Applying AI Principles with Google Cloud can help you prepare for a career as a Product Manager by teaching you the principles of AI and machine learning. You will also learn how to build and deploy AI models on the Google Cloud Platform.
Business Analyst
Business Analyst
Business Analysts help businesses understand their business needs and develop solutions to meet those needs. They work with stakeholders to gather and analyze data, and develop and implement solutions. Responsible AI: Applying AI Principles with Google Cloud can help you prepare for a career as a Business Analyst by teaching you the principles of AI and machine learning. You will also learn how to build and deploy AI models on the Google Cloud Platform.
Project Manager
Project Manager
Project Managers plan and execute projects. They work with stakeholders to define project goals, develop project plans, and track project progress. Responsible AI: Applying AI Principles with Google Cloud can help you prepare for a career as a Project Manager by teaching you the principles of AI and machine learning. You will also learn how to build and deploy AI models on the Google Cloud Platform.
Data Science Manager
Data Science Manager
Data Science Managers lead teams of data scientists and other professionals. They work with stakeholders to define data science goals, develop data science strategies, and manage data science projects. Responsible AI: Applying AI Principles with Google Cloud can help you prepare for a career as a Data Science Manager by teaching you the principles of AI and machine learning. You will also learn how to build and deploy AI models on the Google Cloud Platform.
Machine Learning Manager
Machine Learning Manager
Machine Learning Managers lead teams of machine learning engineers and other professionals. They work with stakeholders to define machine learning goals, develop machine learning strategies, and manage machine learning projects. Responsible AI: Applying AI Principles with Google Cloud can help you prepare for a career as a Machine Learning Manager by teaching you the principles of AI and machine learning. You will also learn how to build and deploy AI models on the Google Cloud Platform.
Chief Data Officer
Chief Data Officer
Chief Data Officers lead data science and analytics teams. They work with stakeholders to define data strategy, develop data governance policies, and manage data assets.
Chief Analytics Officer
Chief Analytics Officer
Chief Analytics Officers lead analytics teams. They work with stakeholders to define analytics strategy, develop analytics governance policies, and manage analytics assets.
Chief AI Officer
Chief AI Officer
Chief AI Officers lead AI teams. They work with stakeholders to define AI strategy, develop AI governance policies, and manage AI assets.
Senior Data Scientist
Senior Data Scientist
Senior Data Scientists develop and deploy AI models. They work with other data scientists and engineers to build and maintain AI systems. This course can help prepare you for a career as a Senior Data Scientist by teaching you the principles of AI and machine learning. You will also learn how to build and deploy AI models on the Google Cloud Platform.
Senior Machine Learning Engineer
Senior Machine Learning Engineer
Senior Machine Learning Engineers develop and deploy AI models. They work with other engineers and data scientists to build and maintain AI systems. This course can help prepare you for a career as a Senior Machine Learning Engineer by teaching you the principles of AI and machine learning. You will also learn how to build and deploy AI models on the Google Cloud Platform.
For more career information including salaries, visit:
OpenCourser.com/course/449shb/responsible
Reading list
We've selected nine 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
Responsible AI: Applying AI Principles with GC - Français.
Save
Provides practical guidelines for designing and developing AI systems in an ethical and responsible manner.
Explores the challenges of developing AI systems that are safe and beneficial for humanity.
Save
Explores the potential risks and benefits of developing superintelligent AI systems.
Offers a comprehensive overview of the ethical and social issues that arise from the development and use of AI.
Save
Explores the potential future of humanity in light of the development of AI and other emerging technologies.
Save
Explores the potential impact of AI on human society and the future of humanity.
Save
Classic in the field of deep learning and provides a comprehensive overview of the topic.
Save
Provides a thought-provoking exploration of the future of AI and its potential impact on society.
Save
Provides a comprehensive overview of the technical aspects of AI and machine learning, which can be useful for understanding the underlying concepts of the course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/449shb/responsible
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