基于区块链的毕业设计cs774-ethics-fall-2020 (Edu 4.0) – cs774-ethics-fall-2020(教育单元4.0)

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cs774-ethics-fall-2020 (Edu 4.0)

Please send email to cs774.ethics.uilab@gmail.com regarding any class-related issues, instead of the professor’s email.

Teaching Staff

  • Lecturer: Alice Oh
  • TA: Jaimeen Ahn
  • Contact: cs774.ethics.uilab@gmail.com

Please send email to cs774.ethics.uilab@gmail.com. We will not consider any class-related email arriving in our personal accounts. When you send emails, please put “[CS774]” to the title. (e.g., [CS774] Do we have a class on MM/DD?)

Time & Location

  • Mon/Wed 13:00 – 14:30
  • #2111, E3-1 (Information Science and Electronics Bldg.) ZOOM (link: TBA)
  • If there is a guest lecture, lecture time may change flexibly such as 4:00pm ~ 5:30pm

Prerequisites

  • Knowledge of machine learning, and deep learning (CS570)

Schedule (Subject to Change)

week Day Type Topic notes Project
1 08/31, 09/02 Introduction, Bias of AI/ML Systems Online Lecture
2 09/07, 09/09 Lecture 1
Discussion 1
Bias of AI/ML Systems
3 09/14, 09/16 Lecture 2
Discussion 2
Effect on Jobs and Economy
4 09/21, 09/23 Discussion 3
Discussion 4
Effect on Jobs and Economy
5 09/28, 09/30 Project Project Description 09/30 Holiday Introduction, Team matching
6 10/05, 10/07 Lecture 3
Discussion 5
AI for Social Good 10/07 Guest Lecture 4:00pm
Joanna Bryson
7 10/12, 10/14 Discussion 6
Discussion 7
AI for Social Good
8 10/19, 10/22 Mid-term Proposal, Peer-review
9 10/26, 10/28 Lecture 4
Discussion 8
NLP for detecting Bias
10 11/02, 11/04 Discussion 9
Discussion 10
NLP for detecting Bias 11/04 Guest Lecture 4:00pm
Dirk Hovy
11 11/09, 11/11 Lecture 5
Discussion 11
AI as Big Brother
12 11/16, 11/18 Discussion 12
Discussion 13
AI as Big Brother Progress Update, Peer-review
13 11/23, 11/25 Lecture 6
Discussion 14
Interpretability and Fairness
14 11/30, 12/02 Lecture 15
Discussion 16
Interpretability and Fairness
15 12/07, 12/09 Project presentation
16 12/14, 12/16 No final No Class Final presentation Peer-review

Course

The course consists of lectures and discussions.

Special Lecture

Experts from around the world in AI and Ethics will give special virtual lectures.
Most of the lectures will be moderated by the main lecturer (Alice Oh) in the form of questions and answers about the lecturers’ publications.
Because of the time difference, some lectures will be pre-recorded.
Possible lecturers include Joanna Bryson (Hertie School) on the topic of general AI Ethics, Shakir Mohamed (DeepMind) on the topic of diversity and inclusion in AI, Dirk Hovy(Bocconi University) on the topic of Predictive Bias in NLP, and additional guests will be added.

Reading

Students will read, present, and think about latest research from the reading list which is published in AI and ML conferences (e.g., NeurIPS, ICLR, ACL, CVPR, FAccT) related to ethical considerations.
Readings may also include blog posts, articles in the media, online forum discussions, and publications from global governing bodies.

  • Choose a paper related to the subject of the previous lecture from the reading list (editing …)
  • Read the paper before the discussion and prepare some questions to be discussed

Discussion

Students will lead peers to discuss the readings with thought-provoking questions.
You will challenge the findings in the articles as to their accurate reporting and interpretation; you will discuss relevance to the current time and various locales with different cultural backgrounds.
You will present and discuss ideas for future research directions in AI and ethics.

  • 16 in-class discussion (see schedule)
  • Organize a group of 3~4 people, and have time to present what you read and discuss (you can use Korean if everyone is comfortable with Korean)
  • (All/Half) of groups present your discussion result to the peers at the end of class (depends on registration) and submit your questions and answers as pdf file until midnight.

Team Projects

  • Team project will be a major part of the class, especially during the second half
  • Projects will be basically replication/modifications of recent research in bias in AI/ML
  • More details will be described in the document below
  • https://uilab-kaist.github.io/cs774-ethics-fall-2020/project (Not available right now)

Evaluation

If you actively and honestly participate in every discussion and project you will get at least B

  • 16 In-Class Discussion : 40%

  • Project : 50%

    Note that any team may get up to -25%p for project score if there is a serious problem with teamwork.

  • Peer grading : 10%

Additional references

  • Fairness in Machine Learning by Solon Barocas, Moritz Hardt, and Arvind Narayanan

cs774-ethics-fall-2020 (Edu 4.0)

Please send email to cs774.ethics.uilab@gmail.com regarding any class-related issues, instead of the professor’s email.

Teaching Staff

  • Lecturer: Alice Oh
  • TA: Jaimeen Ahn
  • Contact: cs774.ethics.uilab@gmail.com

Please send email to cs774.ethics.uilab@gmail.com. We will not consider any class-related email arriving in our personal accounts. When you send emails, please put “[CS774]” to the title. (e.g., [CS774] Do we have a class on MM/DD?)

Time & Location

  • Mon/Wed 13:00 – 14:30
  • #2111, E3-1 (Information Science and Electronics Bldg.) ZOOM (link: TBA)
  • If there is a guest lecture, lecture time may change flexibly such as 4:00pm ~ 5:30pm

Prerequisites

  • Knowledge of machine learning, and deep learning (CS570)

Schedule (Subject to Change)

week Day Type Topic notes Project
1 08/31, 09/02 Introduction, Bias of AI/ML Systems Online Lecture
2 09/07, 09/09 Lecture 1
Discussion 1
Bias of AI/ML Systems
3 09/14, 09/16 Lecture 2
Discussion 2
Effect on Jobs and Economy
4 09/21, 09/23 Discussion 3
Discussion 4
Effect on Jobs and Economy
5 09/28, 09/30 Project Project Description 09/30 Holiday Introduction, Team matching
6 10/05, 10/07 Lecture 3
Discussion 5
AI for Social Good 10/07 Guest Lecture 4:00pm
Joanna Bryson
7 10/12, 10/14 Discussion 6
Discussion 7
AI for Social Good
8 10/19, 10/22 Mid-term Proposal, Peer-review
9 10/26, 10/28 Lecture 4
Discussion 8
NLP for detecting Bias
10 11/02, 11/04 Discussion 9
Discussion 10
NLP for detecting Bias 11/04 Guest Lecture 4:00pm
Dirk Hovy
11 11/09, 11/11 Lecture 5
Discussion 11
AI as Big Brother
12 11/16, 11/18 Discussion 12
Discussion 13
AI as Big Brother Progress Update, Peer-review
13 11/23, 11/25 Lecture 6
Discussion 14
Interpretability and Fairness
14 11/30, 12/02 Lecture 15
Discussion 16
Interpretability and Fairness
15 12/07, 12/09 Project presentation
16 12/14, 12/16 No final No Class Final presentation Peer-review

Course

The course consists of lectures and discussions.

Special Lecture

Experts from around the world in AI and Ethics will give special virtual lectures.
Most of the lectures will be moderated by the main lecturer (Alice Oh) in the form of questions and answers about the lecturers’ publications.
Because of the time difference, some lectures will be pre-recorded.
Possible lecturers include Joanna Bryson (Hertie School) on the topic of general AI Ethics, Shakir Mohamed (DeepMind) on the topic of diversity and inclusion in AI, Dirk Hovy(Bocconi University) on the topic of Predictive Bias in NLP, and additional guests will be added.

Reading

Students will read, present, and think about latest research from the reading list which is published in AI and ML conferences (e.g., NeurIPS, ICLR, ACL, CVPR, FAccT) related to ethical considerations.
Readings may also include blog posts, articles in the media, online forum discussions, and publications from global governing bodies.

  • Choose a paper related to the subject of the previous lecture from the reading list (editing …)
  • Read the paper before the discussion and prepare some questions to be discussed

Discussion

Students will lead peers to discuss the readings with thought-provoking questions.
You will challenge the findings in the articles as to their accurate reporting and interpretation; you will discuss relevance to the current time and various locales with different cultural backgrounds.
You will present and discuss ideas for future research directions in AI and ethics.

  • 16 in-class discussion (see schedule)
  • Organize a group of 3~4 people, and have time to present what you read and discuss (you can use Korean if everyone is comfortable with Korean)
  • (All/Half) of groups present your discussion result to the peers at the end of class (depends on registration) and submit your questions and answers as pdf file until midnight.

Team Projects

  • Team project will be a major part of the class, especially during the second half
  • Projects will be basically replication/modifications of recent research in bias in AI/ML
  • More details will be described in the document below
  • https://uilab-kaist.github.io/cs774-ethics-fall-2020/project (Not available right now)

Evaluation

If you actively and honestly participate in every discussion and project you will get at least B

  • 16 In-Class Discussion : 40%

  • Project : 50%

    Note that any team may get up to -25%p for project score if there is a serious problem with teamwork.

  • Peer grading : 10%

Additional references

  • Fairness in Machine Learning by Solon Barocas, Moritz Hardt, and Arvind Narayanan

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