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Responsible AI for Developers

Fairness & Bias - 日本語版

Google Cloud Training

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。

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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
AI における公平性とバイアス
このモジュールでは、AI における公平性とバイアスに焦点を当てます。データとモデリングを通じたバイアスの識別と軽減のためのさまざまな手法やツールを紹介します。
コースのまとめ
このモジュールでは、最も重要なコンセプト、ツール、手法について取り上げ、コース全体の概要を説明します。
コースのリソース
すべてのモジュールへの受講者用 PDF リンク

Good to know

Know what's good
, what to watch for
, 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。

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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 - 日本語版 with these activities:
Review the Google AI Platform roadmap
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.

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Course image
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Session
August 5, 2024 - September 16, 2024
Language
日本語
Transcript
English
Good to know
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。
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