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本課程旨在說明負責任 AI 技術的概念和 AI 開發原則，同時介紹各項技術，在實務上找出公平性和偏誤，減少 AI/機器學習做法上的偏誤。我們也將探討實用方法和工具，透過 Google Cloud 產品和開放原始碼工具，導入負責任 AI 技術的最佳做法。
Register for this course and see more details by visiting:
OpenCourser.com/course/2yphp6/responsible
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What's inside
Syllabus
課程簡介
本單元介紹課程架構和目標。
負責任的 AI 技術簡介
本單元簡介負責任的 AI 技術，探討 Google 的 AI 開發原則和負責任 AI 技術的子議題，同時也對 Google 產品中的負責任 AI 技術提供實際個案研究。
Read more
Syllabus
課程簡介
本單元介紹課程架構和目標。
負責任的 AI 技術簡介
本單元簡介負責任的 AI 技術，探討 Google 的 AI 開發原則和負責任 AI 技術的子議題，同時也對 Google 產品中的負責任 AI 技術提供實際個案研究。
Read more
AI 公平性和偏誤
本單元著重介紹 AI 公平性和偏誤，並提供多種技術與工具，透過資料和模型找出並減少偏誤。
課程摘要
本單元介紹最重要的概念、工具和技術，概略說明整個課程內容。
課程資源
所有單元的學員用 PDF 連結
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
針對 AI 開發者和專業人士而設計，提供實務技術和工具來協助他們建構負責任的人工智慧 (AI)，減少偏見並確保公平性。
透過 Google Cloud 產品和開源工具，介紹導入負責任 AI 技術的最佳做法，實務性強。
Save this course
Save Responsible AI for Developers: Fairness & Bias - 繁體中文 to your list so you can find it easily later:
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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 - 繁體中文 with these
activities:
Compile notes, assignments, and other course materials
Show steps
Organizing and reviewing your learning materials will help you retain and recall the key concepts covered in this course on responsible AI.
Show steps
-
Gather all the notes, assignments, and other materials from this course.
-
Organize the materials into a logical structure.
-
Review the materials regularly to reinforce your understanding.
Review concepts of fairness in AI
Show steps
Review the basic principles of fairness in AI, as these concepts provide the foundation for this course on responsible AI.
Browse courses on
Fairness in AI
Show steps
-
Read up on the concept of fairness in AI, such as in the book Fairness and Machine Learning by Solon Barocas and Andrew Selbst.
-
Consider how fairness might manifest in different AI applications.
-
Discuss the importance of fairness in AI with colleagues or classmates.
Review the basics of AI and machine learning
Show steps
A solid foundation in AI and machine learning will enhance your understanding of responsible AI.
Show steps
-
Read a book or take a course on the basics of AI and machine learning.
-
Review your notes or materials from previous courses on AI and machine learning.
-
Practice implementing simple AI and machine learning algorithms.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Join a study group or discussion forum on responsible AI
Show steps
Engaging with peers can strengthen your understanding of responsible AI and provide diverse perspectives.
Show steps
-
Find a study group or discussion forum dedicated to responsible AI.
-
Actively participate in discussions and share your insights.
-
Seek feedback and support from other group members.
與同學組成小組，討論負責任 AI 的道德考量
Show steps
與同學討論負責任 AI 的道德考量，有助於培養批判性思維和溝通能力。
Browse courses on
Ethics in AI
Show steps
-
組成學習小組
-
選擇負責任 AI 道德議題
-
進行小組討論
-
撰寫小組報告
透過 Google Cloud 產品和開放原始碼工具運用負責任 AI 技術
Show steps
了解如何將實務方法和工具結合 Google Cloud 產品和開放原始碼工具，推動負責任 AI 技術。
Browse courses on
Responsible AI
Show steps
-
探索 Google Cloud AI 產品
-
調查 GitHub 等開放原始碼資源
-
觀看線上教學影片和文件
Practice identifying biases in datasets
Show steps
Identifying biases in datasets is an important skill for responsible AI development.
Browse courses on
Bias in AI
Show steps
-
Use a tool like the Fairness 360 toolkit to identify biases in a dataset.
-
Manually analyze a dataset for potential biases.
-
Discuss the potential impact of biases in AI systems.
找出資料中潛在的誤差並說明
Show steps
練習找出資料常見的錯誤類型，有助於提升辨識和處理 AI 模型中可能出現的偏誤。
Browse courses on
AI Fairness
Show steps
-
收集不同類型的資料集
-
深入探討資料，找出錯誤
-
分析錯誤類型並說明其原因
設計一個 AI 系統並說明如何避免偏誤
Show steps
實際設計一個 AI 系統並說明避免偏誤的方法，能強化對理論概念的理解。
Browse courses on
Bias Mitigation
Show steps
-
選擇一個應用領域
-
設計 AI 系統架構
-
說明如何避免偏誤
-
製作簡報檔或原型
Develop a plan for mitigating bias in an AI system
Show steps
This activity allows you to apply the concepts learned in the course to a real-world scenario by developing a plan to address bias in an AI system.
Show steps
-
Identify a specific AI system or application.
-
Analyze the system for potential sources of bias.
-
Develop a plan for mitigating the identified biases.
-
Present your plan to a group of peers or colleagues for feedback.
Volunteer for an organization working on responsible AI
Show steps
Practical experience in the field of responsible AI will give you invaluable insights and connections.
Show steps
-
Research organizations working on responsible AI.
-
Identify opportunities to volunteer your skills and knowledge.
-
Attend events and workshops organized by these organizations.
Start a project to implement a responsible AI solution
Show steps
Putting theory into practice is a great way to enhance your understanding and skills in responsible AI.
Show steps
-
Define the scope and objectives of your project.
-
Choose a dataset and AI algorithm for your project.
-
Implement your AI solution while considering responsible AI principles.
Compile notes, assignments, and other course materials
Show steps
Organizing and reviewing your learning materials will help you retain and recall the key concepts covered in this course on responsible AI.
Show steps
Show steps
Show steps
- Gather all the notes, assignments, and other materials from this course.
- Organize the materials into a logical structure.
- Review the materials regularly to reinforce your understanding.
Review concepts of fairness in AI
Show steps
Review the basic principles of fairness in AI, as these concepts provide the foundation for this course on responsible AI.
Browse courses on
Fairness in AI
Show steps
Show steps
Show steps
- Read up on the concept of fairness in AI, such as in the book Fairness and Machine Learning by Solon Barocas and Andrew Selbst.
- Consider how fairness might manifest in different AI applications.
- Discuss the importance of fairness in AI with colleagues or classmates.
Review the basics of AI and machine learning
Show steps
A solid foundation in AI and machine learning will enhance your understanding of responsible AI.
Show steps
Show steps
Show steps
- Read a book or take a course on the basics of AI and machine learning.
- Review your notes or materials from previous courses on AI and machine learning.
- Practice implementing simple AI and machine learning algorithms.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Join a study group or discussion forum on responsible AI
Show steps
Engaging with peers can strengthen your understanding of responsible AI and provide diverse perspectives.
Show steps
Show steps
Show steps
- Find a study group or discussion forum dedicated to responsible AI.
- Actively participate in discussions and share your insights.
- Seek feedback and support from other group members.
與同學組成小組，討論負責任 AI 的道德考量
Show steps
與同學討論負責任 AI 的道德考量，有助於培養批判性思維和溝通能力。
Browse courses on
Ethics in AI
Show steps
Show steps
Show steps
- 組成學習小組
- 選擇負責任 AI 道德議題
- 進行小組討論
- 撰寫小組報告
透過 Google Cloud 產品和開放原始碼工具運用負責任 AI 技術
Show steps
了解如何將實務方法和工具結合 Google Cloud 產品和開放原始碼工具，推動負責任 AI 技術。
Browse courses on
Responsible AI
Show steps
Show steps
Show steps
- 探索 Google Cloud AI 產品
- 調查 GitHub 等開放原始碼資源
- 觀看線上教學影片和文件
Practice identifying biases in datasets
Show steps
Identifying biases in datasets is an important skill for responsible AI development.
Browse courses on
Bias in AI
Show steps
Show steps
Show steps
- Use a tool like the Fairness 360 toolkit to identify biases in a dataset.
- Manually analyze a dataset for potential biases.
- Discuss the potential impact of biases in AI systems.
找出資料中潛在的誤差並說明
Show steps
練習找出資料常見的錯誤類型，有助於提升辨識和處理 AI 模型中可能出現的偏誤。
Browse courses on
AI Fairness
Show steps
Show steps
Show steps
- 收集不同類型的資料集
- 深入探討資料，找出錯誤
- 分析錯誤類型並說明其原因
設計一個 AI 系統並說明如何避免偏誤
Show steps
實際設計一個 AI 系統並說明避免偏誤的方法，能強化對理論概念的理解。
Browse courses on
Bias Mitigation
Show steps
Show steps
Show steps
- 選擇一個應用領域
- 設計 AI 系統架構
- 說明如何避免偏誤
- 製作簡報檔或原型
Develop a plan for mitigating bias in an AI system
Show steps
This activity allows you to apply the concepts learned in the course to a real-world scenario by developing a plan to address bias in an AI system.
Show steps
Show steps
Show steps
- Identify a specific AI system or application.
- Analyze the system for potential sources of bias.
- Develop a plan for mitigating the identified biases.
- Present your plan to a group of peers or colleagues for feedback.
Volunteer for an organization working on responsible AI
Show steps
Practical experience in the field of responsible AI will give you invaluable insights and connections.
Show steps
Show steps
Show steps
- Research organizations working on responsible AI.
- Identify opportunities to volunteer your skills and knowledge.
- Attend events and workshops organized by these organizations.
Start a project to implement a responsible AI solution
Show steps
Putting theory into practice is a great way to enhance your understanding and skills in responsible AI.
Show steps
Show steps
Show steps
- Define the scope and objectives of your project.
- Choose a dataset and AI algorithm for your project.
- Implement your AI solution while considering responsible AI principles.
Career center
Learners who complete Responsible AI for Developers: Fairness & Bias - 繁體中文 will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/2yphp6/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/2yphp6/responsible
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Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Session
August 5, 2024
- September 16, 2024
Language
zh-TW
Good to know
針對 AI 開發者和專業人士而設計，提供實務技術和工具來協助他們建構負責任的人工智慧 (AI)，減少偏見並確保公平性。
透過 Google Cloud 產品和開源工具，介紹導入負責任 AI 技術的最佳做法，實務性強。
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