Reading 3: FAccT AI & ML

Reading Theme

Machine learning (ML)-based AI systems, especially Deep Learning (DL), have demonstrated remarkable learning capabilities. Unfortunately, current AI systems are inherently hard to gain trust from their users. With responding to this issue, a lot of ML/DL researchers have been focusing on improving the Fairness, Accountability, and Transparency (FAccT) of ML/DL models in addition to their performance. 

More recently, FAccT is a fast-growing and important area of the ML/DL research.

Objective

To develop research thinking in the field of Fair, Accountable, and Transparent (FAccT) machine learning. 

References

• Christopher M. Bishop, Pattern Recognition and Machine Learning, 2006

• Barocas, Solon, Moritz Hardt, and Arvind Narayanan. Fairness and Machine Learning, 2018.

• Molnar, Christoph. Interpretable machine learning, 2019.

Information

• Date and Time: January 9 - 11, 2024 from 15:30 to 17:00

• Place: IS 7階輪講室/7F Seminar Room

All speakers should share your presentation via this shared folder beforehand. 

Outline

• Day 1: Bias and Fairness && Interpretability and Transparency

  • Chapter 4 of Barocas (Speaker: Khine Myat Thwe)

  • Chapter 3 of Molnar (Speaker: Prabhat Parajuli)

• Day 2: Interpretability and Transparency && Local Model-Agnostic Methods 

  • Chapter 5 of Molnar (Speaker: Ziheng Zhong)

  • Section 9.2 of Molnar (Speaker: Suphawit Xu)

• Day 3: Local Model-Agnostic Methods && Neural Network Interpretation

  • Section 9.5 of Molnar (Speaker: Suphawit Xu)

  • Section 9.6 of Molnar (Speaker: Suphawit Xu)

  • Chapter 10 of Molnar (Speaker: Chayanee Junplong)