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このコースでは、責任ある AI および AI に関する原則のコンセプトを紹介します。AI / ML の実践における公平性とバイアスを特定し、バイアスを軽減するための実践的な手法を取り扱います。具体的には、Google Cloud プロダクトとオープンソース ツールを使用して責任ある AI のベスト プラクティスを実装するための実践的な方法とツールを検証します。
Register for this course and see more details by visiting:
OpenCourser.com/course/6ye4zq/responsible
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What's inside
Syllabus
コース概要
このモジュールでは、コースの構成と目標について説明します。
責任ある AI の概要
このモジュールでは、責任ある AI の概要について説明します。内容には、Google の AI に関する原則と、責任ある AI に関するサブトピックが含まれます。また、Google プロダクトにおける責任ある AI の実際のケーススタディも紹介します。
Read more
Syllabus
コース概要
このモジュールでは、コースの構成と目標について説明します。
責任ある AI の概要
このモジュールでは、責任ある AI の概要について説明します。内容には、Google の AI に関する原則と、責任ある AI に関するサブトピックが含まれます。また、Google プロダクトにおける責任ある AI の実際のケーススタディも紹介します。
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
対象者が Google Cloud を使用する前提のコースです。
AI における公平性とバイアスに対処するベストプラクティスを扱っています。
責任ある AI 実践に取り組みたい人に向いています。
Google Cloud プラットフォームの知識があるとより理解しやすいでしょう。
AI と機械学習の背景知識があると理解を深められます。
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
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Reviews summary
責任あるai開発のための公平性入門
学習者によると、本コースは責任あるAIの原則とAIにおける公平性およびバイアスについて包括的かつ実践的な入門を提供しています。特にAI/ML開発者を対象としており、明確で論理的なカリキュラム構成が広く評価されています。多くの受講者が、Google Cloudプロダクトやオープンソースツールを用いた具体的なバイアス特定と軽減手法の解説が非常に実践的であるとコメントしており、実務への応用可能性が高い点を強調しています。このコースは、単なる概念的な理解に留まらず、AIシステムにおける倫理的な課題に技術的にどのように対処するかを具体的に示している点がユニークな強みです。一方で、一部の経験豊富な開発者や研究者は、より高度なアルゴリズムや複雑なケーススタディ、あるいはGoogleエコシステム外の多様な事例について、さらなる深掘りを期待する声も挙がっています。コース全体を通して、日本語の翻訳品質が高く、専門用語も適切であるため、日本の学習者にとって理解しやすい環境が提供されていると概ね肯定的に受け止められています。全体として、AI開発における倫理的責任の重要性を認識し、実践的なスキルを身につけたいと考える多くの学習者にとって価値あるコースであると評価されています。
日本語の翻訳と表現が自然で理解しやすい。
"日本語版として非常に質が高く、専門用語も適切に翻訳されていてストレスなく学べました。"
"日本語での解説が自然で、海外のコースにありがちな違和感がなく、内容に集中できました。"
"日本語で責任あるAIの概念を体系的に学べる機会は貴重であり、このコースは期待以上でした。"
内容が分かりやすく整理されており、理解しやすい。
"コースの構成が論理的で、トピックが順を追って説明されているため、非常に理解しやすかった。"
"複雑なテーマにもかかわらず、説明が非常に明快で、難なくついていくことができました。"
"モジュールごとに重要なポイントがまとめられており、効率的に学習を進めることができました。"
Google CloudとOSSツールを用いた実用的な手法を学ぶ。
"Google Cloudのプロダクトやオープンソースツールを使った実践的なアプローチがとても参考になりました。"
"バイアス軽減のための具体的なツールと技術デモがあり、すぐに業務に活かせそうです。"
"抽象的な概念だけでなく、実際にどうコードに落とし込むかが見えて、私には非常に有益でした。"
AI倫理と公平性の原則を明確に紹介しています。
"責任あるAIの基本的な考え方とGoogleの原則を理解する上で非常に役立ちました。"
"AIにおける公平性とバイアスに関する導入として、非常に分かりやすく、概念を把握できました。"
"このコースは、責任あるAI開発の重要性を認識し、その原則を学ぶための良い出発点だと感じました。"
一部のトピックはより詳細な説明が必要。
"基礎を学ぶには良いが、より高度なバイアス軽減アルゴリズムや複雑なケーススタディが欲しかった。"
"Googleのツールに特化しすぎている感があり、他のフレームワークにおける応用例も知りたいと思いました。"
"特定の技術的な詳細については、もう少し踏み込んだ解説があれば、より満足できたでしょう。"
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 - 日本語版 with these
activities:
Review the Google AI Platform roadmap
Show steps
Reviewing the roadmap will orient you with the current state and planned future of AI Platform.
Browse courses on
Google Cloud AI Platform
Show steps
-
Visit the AI Platform roadmap page
-
Read through the roadmap
-
Identify the key areas of development
-
Consider how these developments may impact your work
Review core AI concepts
Show steps
Begin the course with a clear foundation of core AI concepts to maximize understanding of subsequent lessons.
Browse courses on
AI
Show steps
-
Read textbooks or online materials on AI, machine learning, and deep learning.
-
Complete practice problems or exercises related to core AI concepts.
Review core AI concepts
Show steps
Strengthen your understanding of fundamental AI concepts to enhance comprehension of course material.
Browse courses on
Machine Learning
Show steps
-
Review basic machine learning algorithms, such as linear regression and decision trees.
-
Familiarize yourself with essential AI terminology and frameworks.
14 other activities
Expand to see all activities and additional details
Show all 17 activities
Review the basics of data science and machine learning
Show steps
Refreshes the foundational knowledge and concepts in data science and machine learning, providing a solid base for understanding the principles of responsible AI.
Browse courses on
Data Science
Show steps
-
Review introductory materials on data science and machine learning
-
Complete practice exercises to reinforce understanding
Engage in peer discussions
Show steps
Enhance your understanding by sharing knowledge and perspectives with fellow learners.
Show steps
-
Join online discussion forums or study groups related to the course topics.
-
Participate in discussions, ask questions, and share your insights.
Engage in peer discussions on AI bias and fairness
Show steps
Foster collaborative learning by engaging in discussions with peers to challenge ideas and perspectives on AI bias and fairness.
Browse courses on
Bias in AI
Show steps
-
Form or join a study group with fellow learners.
-
Choose a specific topic related to AI bias or fairness for discussion.
-
Prepare research or case studies to support your arguments.
-
Participate actively in discussions, listening attentively to others' perspectives.
Attend a workshop on AI ethics and governance
Show steps
Broaden your perspective and delve deeper into the ethical and regulatory aspects of AI through an interactive workshop.
Browse courses on
AI Ethics
Show steps
-
Identify and register for a relevant workshop on AI ethics or governance.
-
Attend the workshop and actively participate in discussions and exercises.
-
Reflect on the key takeaways and implications for your own AI development practices.
Participate in group discussions on AI ethics and responsible AI
Show steps
Fosters collaboration and critical thinking by engaging students in discussions on the ethical implications and societal impact of AI.
Browse courses on
AI Ethics
Show steps
-
Prepare for discussions by reading assigned materials and researching current events related to AI ethics
-
Actively engage in group discussions, sharing insights and perspectives
-
Reflect on discussions and consider implications for personal and professional practice
Explore Google Cloud AI Platform
Show steps
Familiarize yourself with Google Cloud AI Platform to apply theoretical concepts practically.
Browse courses on
Google Cloud AI Platform
Show steps
-
Follow tutorials provided by Google Cloud to set up and use AI Platform services.
-
Experiment with different services to understand their capabilities.
Practice identifying and mitigating bias in datasets
Show steps
Enhance your skills in detecting and addressing bias in datasets to ensure ethical AI implementation.
Browse courses on
Bias Mitigation
Show steps
-
Analyze real-world datasets for potential biases.
-
Apply techniques for bias mitigation, such as resampling and data augmentation.
-
Evaluate the effectiveness of bias mitigation strategies.
Practice identifying and mitigating bias in AI datasets
Show steps
Provides hands-on experience in identifying and mitigating bias in AI datasets, developing skills essential for creating fair and equitable AI systems.
Browse courses on
Bias in AI
Show steps
-
Work through guided tutorials on bias identification and mitigation
-
Analyze real-world datasets and identify potential biases
Explore tutorials on AI best practices and standards
Show steps
Enhance your knowledge of industry-recognized AI best practices and standards to ensure ethical and effective AI development.
Browse courses on
AI Best Practices
Show steps
-
Identify reputable sources and platforms for AI tutorials.
-
Select tutorials that cover topics aligned with course content.
-
Follow tutorials meticulously, taking notes and implementing the techniques learned.
Contribute to an open-source project related to responsible AI
Show steps
Provides practical experience in applying the principles of responsible AI and contributing to the broader community working towards ethical AI development.
Browse courses on
Responsible AI
Show steps
-
Identify an open-source project focused on responsible AI
-
Contribute to the project by reporting bugs, writing documentation, or contributing code
-
Collaborate with project maintainers and other contributors
Develop a sample AI project
Show steps
Reinforce your understanding by creating a hands-on AI project that incorporates concepts learned in the course.
Show steps
-
Identify a problem or area where AI can be applied.
-
Design and implement an AI solution using concepts learned in the course.
-
Document your project, including the problem, solution, and results.
Develop a case study on responsible AI implementation
Show steps
Gain practical experience in applying responsible AI principles through a hands-on case study.
Show steps
-
Identify an industry or domain where responsible AI is critical.
-
Research and analyze best practices for responsible AI in the chosen domain.
-
Design and implement a hypothetical AI system that incorporates responsibility principles.
-
Evaluate the effectiveness and impact of the designed AI system.
Create a presentation on a case study of responsible AI implementation
Show steps
Encourages critical thinking and communication skills by requiring students to research and present on real-world examples of responsible AI implementation.
Browse courses on
Responsible AI
Show steps
-
Research and select a case study of responsible AI implementation
-
Develop a presentation outlining the principles, practices, and outcomes of the case study
-
Present the case study to classmates and instructors
Contribute to open-source projects related to responsible AI
Show steps
Gain practical experience in contributing to the advancement of responsible AI by engaging with open-source projects.
Browse courses on
Responsible AI Development
Show steps
-
Identify open-source projects focused on responsible AI principles and practices.
-
Review project documentation and codebases.
-
Identify areas where you can make meaningful contributions.
-
Collaborate with project maintainers and other contributors.
-
Submit code contributions, bug reports, or documentation improvements.
Review the Google AI Platform roadmap
Show steps
Reviewing the roadmap will orient you with the current state and planned future of AI Platform.
Browse courses on
Google Cloud AI Platform
Show steps
Show steps
Show steps
- Visit the AI Platform roadmap page
- Read through the roadmap
- Identify the key areas of development
- Consider how these developments may impact your work
Review core AI concepts
Show steps
Begin the course with a clear foundation of core AI concepts to maximize understanding of subsequent lessons.
Browse courses on
AI
Show steps
Show steps
Show steps
- Read textbooks or online materials on AI, machine learning, and deep learning.
- Complete practice problems or exercises related to core AI concepts.
Review core AI concepts
Show steps
Strengthen your understanding of fundamental AI concepts to enhance comprehension of course material.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review basic machine learning algorithms, such as linear regression and decision trees.
- Familiarize yourself with essential AI terminology and frameworks.
14 other activities
Expand to see all activities and additional details
Show all 17 activities
Review the basics of data science and machine learning
Show steps
Refreshes the foundational knowledge and concepts in data science and machine learning, providing a solid base for understanding the principles of responsible AI.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Review introductory materials on data science and machine learning
- Complete practice exercises to reinforce understanding
Engage in peer discussions
Show steps
Enhance your understanding by sharing knowledge and perspectives with fellow learners.
Show steps
Show steps
Show steps
- Join online discussion forums or study groups related to the course topics.
- Participate in discussions, ask questions, and share your insights.
Engage in peer discussions on AI bias and fairness
Show steps
Foster collaborative learning by engaging in discussions with peers to challenge ideas and perspectives on AI bias and fairness.
Browse courses on
Bias in AI
Show steps
Show steps
Show steps
- Form or join a study group with fellow learners.
- Choose a specific topic related to AI bias or fairness for discussion.
- Prepare research or case studies to support your arguments.
- Participate actively in discussions, listening attentively to others' perspectives.
Attend a workshop on AI ethics and governance
Show steps
Broaden your perspective and delve deeper into the ethical and regulatory aspects of AI through an interactive workshop.
Browse courses on
AI Ethics
Show steps
Show steps
Show steps
- Identify and register for a relevant workshop on AI ethics or governance.
- Attend the workshop and actively participate in discussions and exercises.
- Reflect on the key takeaways and implications for your own AI development practices.
Participate in group discussions on AI ethics and responsible AI
Show steps
Fosters collaboration and critical thinking by engaging students in discussions on the ethical implications and societal impact of AI.
Browse courses on
AI Ethics
Show steps
Show steps
Show steps
- Prepare for discussions by reading assigned materials and researching current events related to AI ethics
- Actively engage in group discussions, sharing insights and perspectives
- Reflect on discussions and consider implications for personal and professional practice
Explore Google Cloud AI Platform
Show steps
Familiarize yourself with Google Cloud AI Platform to apply theoretical concepts practically.
Browse courses on
Google Cloud AI Platform
Show steps
Show steps
Show steps
- Follow tutorials provided by Google Cloud to set up and use AI Platform services.
- Experiment with different services to understand their capabilities.
Practice identifying and mitigating bias in datasets
Show steps
Enhance your skills in detecting and addressing bias in datasets to ensure ethical AI implementation.
Browse courses on
Bias Mitigation
Show steps
Show steps
Show steps
- Analyze real-world datasets for potential biases.
- Apply techniques for bias mitigation, such as resampling and data augmentation.
- Evaluate the effectiveness of bias mitigation strategies.
Practice identifying and mitigating bias in AI datasets
Show steps
Provides hands-on experience in identifying and mitigating bias in AI datasets, developing skills essential for creating fair and equitable AI systems.
Browse courses on
Bias in AI
Show steps
Show steps
Show steps
- Work through guided tutorials on bias identification and mitigation
- Analyze real-world datasets and identify potential biases
Explore tutorials on AI best practices and standards
Show steps
Enhance your knowledge of industry-recognized AI best practices and standards to ensure ethical and effective AI development.
Browse courses on
AI Best Practices
Show steps
Show steps
Show steps
- Identify reputable sources and platforms for AI tutorials.
- Select tutorials that cover topics aligned with course content.
- Follow tutorials meticulously, taking notes and implementing the techniques learned.
Contribute to an open-source project related to responsible AI
Show steps
Provides practical experience in applying the principles of responsible AI and contributing to the broader community working towards ethical AI development.
Browse courses on
Responsible AI
Show steps
Show steps
Show steps
- Identify an open-source project focused on responsible AI
- Contribute to the project by reporting bugs, writing documentation, or contributing code
- Collaborate with project maintainers and other contributors
Develop a sample AI project
Show steps
Reinforce your understanding by creating a hands-on AI project that incorporates concepts learned in the course.
Show steps
Show steps
Show steps
- Identify a problem or area where AI can be applied.
- Design and implement an AI solution using concepts learned in the course.
- Document your project, including the problem, solution, and results.
Develop a case study on responsible AI implementation
Show steps
Gain practical experience in applying responsible AI principles through a hands-on case study.
Show steps
Show steps
Show steps
- Identify an industry or domain where responsible AI is critical.
- Research and analyze best practices for responsible AI in the chosen domain.
- Design and implement a hypothetical AI system that incorporates responsibility principles.
- Evaluate the effectiveness and impact of the designed AI system.
Create a presentation on a case study of responsible AI implementation
Show steps
Encourages critical thinking and communication skills by requiring students to research and present on real-world examples of responsible AI implementation.
Browse courses on
Responsible AI
Show steps
Show steps
Show steps
- Research and select a case study of responsible AI implementation
- Develop a presentation outlining the principles, practices, and outcomes of the case study
- Present the case study to classmates and instructors
Contribute to open-source projects related to responsible AI
Show steps
Gain practical experience in contributing to the advancement of responsible AI by engaging with open-source projects.
Browse courses on
Responsible AI Development
Show steps
Show steps
Show steps
- Identify open-source projects focused on responsible AI principles and practices.
- Review project documentation and codebases.
- Identify areas where you can make meaningful contributions.
- Collaborate with project maintainers and other contributors.
- Submit code contributions, bug reports, or documentation improvements.
Career center
Learners who complete Responsible AI for Developers: Fairness & Bias - 日本語版 will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/6ye4zq/responsible
Reading list
We haven't picked any books for this reading list yet.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6ye4zq/responsible
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