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Google Cloud Training

This course introduces concepts of responsible AI and AI principles. It covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices. It explores practical methods and tools to implement Responsible AI best practices using Google Cloud products and open source tools.

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What's inside

Syllabus

Course Introduction
This module introduces the course structure and objectives.
Introduction to Responsible AI
This module provides an overview of Responsible AI, covering Google’s AI Principles and sub-topics of Responsible AI. Also, this provides actual case studies of Responsible AI in Google products.
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AI Fairness & Bias
This module focuses on AI Fairness and Bias. It provides various techniques and tools to identify and mitigate biases through data and modeling.
Course Summary
This module provides a summary of the entire course by covering the most important concepts, tools, and technologies.
Course Resources
Student PDF links to all modules

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Suitable for students who want to understand the fundamentals of Responsible AI and mitigate bias in AI/ML practices
Created by Google Cloud Training, providing learners with access to industry-leading expertise in AI and machine learning
Utilizes Google Cloud products and open source tools, giving learners hands-on experience with practical applications

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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 for Developers: Fairness & Bias with these activities:
Review Machine Learning Concepts
Refresh your understanding of machine learning concepts to enhance your grasp of AI principles.
Browse courses on Machine Learning
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  • Review notes from previous courses or textbooks.
  • Complete online tutorials or quizzes.
  • Participate in discussions or forums related to machine learning.
Read Ethics of Artificial Intelligence by S. Russell and P. Norvig
Gain a comprehensive understanding of ethical considerations in AI development.
Show steps
  • Purchase or borrow the book.
  • Set aside dedicated reading time.
  • Take notes and highlight key concepts.
Review AI techniques
Review basic AI techniques to strengthen your understanding of Responsible AI principles.
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  • Review the syllabus of the course.
  • Read the assigned textbook chapters.
  • Watch the introductory videos.
Two other activities
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Show all five activities
Join a Study Group for AI Principles
Engage with peers, share insights, and strengthen your understanding of Responsible AI principles.
Browse courses on AI Principles
Show steps
  • Find or create a study group with fellow course participants.
  • Establish regular meeting times and agenda.
  • Discuss course materials, case studies, and current events related to Responsible AI.
Solve AI Fairness and Bias Practice Problems
Sharpen your skills in identifying and addressing AI fairness and bias through practice.
Browse courses on AI Fairness
Show steps
  • Find online resources for AI fairness practice problems.
  • Attempt to solve the problems independently.
  • Review solutions and explanations.

Career center

Learners who complete Responsible AI for Developers: Fairness & Bias will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists are responsible for analyzing data to extract meaningful insights and building machine learning models. This course can help Data Scientists build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for Data Scientists to understand in order to develop responsible AI applications.
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course can help Machine Learning Engineers build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for Machine Learning Engineers to understand in order to develop responsible AI applications.
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. This course can help Software Engineers build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for Software Engineers to understand in order to develop responsible AI applications.
Data Analyst
Data Analysts are responsible for analyzing data to extract meaningful insights. This course can help Data Analysts build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for Data Analysts to understand in order to develop responsible AI applications.
Product Manager
Product Managers are responsible for managing the development and launch of new products and features. This course can help Product Managers build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for Product Managers to understand in order to develop responsible AI products.
Business Analyst
Business Analysts are responsible for analyzing business processes and developing solutions to improve efficiency and effectiveness. This course can help Business Analysts build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for Business Analysts to understand in order to develop responsible AI solutions.
UX Designer
UX Designers are responsible for designing the user experience of products and services. This course can help UX Designers build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for UX Designers to understand in order to design responsible AI experiences.
Project Manager
Project Managers are responsible for planning, executing, and monitoring projects. This course can help Project Managers build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for Project Managers to understand in order to manage responsible AI projects.
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data pipelines. This course can help Data Engineers build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for Data Engineers to understand in order to build responsible AI data pipelines.
Technical Writer
Technical Writers are responsible for creating documentation for software and other technical products. This course can help Technical Writers build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for Technical Writers to understand in order to create responsible AI documentation.
Quality Assurance Analyst
Quality Assurance Analysts are responsible for testing and verifying the quality of software and other technical products. This course can help Quality Assurance Analysts build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for Quality Assurance Analysts to understand in order to test and verify the quality of responsible AI products.
IT Auditor
IT Auditors are responsible for auditing the security and compliance of IT systems. This course can help IT Auditors build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for IT Auditors to understand in order to audit the security and compliance of responsible AI systems.
Compliance Officer
Compliance Officers are responsible for ensuring that organizations comply with laws and regulations. This course can help Compliance Officers build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for Compliance Officers to understand in order to ensure that organizations comply with laws and regulations related to responsible AI.
Risk Manager
Risk Managers are responsible for identifying, assessing, and mitigating risks. This course can help Risk Managers build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for Risk Managers to understand in order to identify, assess, and mitigate the risks of responsible AI.
Policy Analyst
Policy Analysts are responsible for developing and analyzing policies. This course can help Policy Analysts build a foundation in Responsible AI principles and practices, which are becoming increasingly important as AI becomes more widely adopted. The course covers topics such as AI Fairness and Bias, which are essential for Policy Analysts to understand in order to develop and analyze responsible AI policies.

Reading list

We've selected 12 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 for Developers: Fairness & Bias.
Provides a thoughtful and accessible overview of the current state of AI. It valuable resource for anyone who wants to understand the technical and philosophical challenges facing the development of safe and ethical AI.
Provides a clear and accessible introduction to the ethical and societal challenges posed by AI. It valuable resource for anyone who wants to understand the broader context of responsible AI.
Provides a powerful indictment of the use of AI to perpetuate inequality. It valuable resource for anyone who wants to understand the social and political consequences of responsible AI.
Provides a thought-provoking exploration of the future of AI and its impact on humanity. It valuable resource for anyone who wants to think critically about the existential challenges posed by AI.
A comprehensive guide to interpretable machine learning models, techniques, and applications. Explores how to make black box models explainable and provides practical guidance on how to interpret model predictions.
Provides a comprehensive overview of the legal and ethical challenges posed by AI. It valuable resource for anyone who wants to understand the regulatory landscape surrounding AI.
Valuable resource for anyone who wants to implement responsible AI practices in their own work, as it provides a practical guide to using the PyTorch framework for deep learning.
Examines the ethical implications of artificial intelligence. Provides a comprehensive overview of the ethical issues raised by AI and offers guidance on how to address them.
Provides a practical introduction to machine learning using Scikit-Learn, Keras, and TensorFlow. Useful as a companion reference for those who want to apply machine learning techniques in their work.
Provides a practical introduction to deep learning using Python. Useful as a companion reference for those who want to apply deep learning techniques in their work.
Provides a comprehensive overview of machine learning from a probabilistic perspective. A valuable resource for those who want to gain a deeper understanding of the theoretical foundations of machine learning.

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