Test-takers have a say: understanding the implications of the use of AI in language tests

no code implementations • 19 Jul 2023 • Dawen Zhang, Thong Hoang, Shidong Pan, Yongquan Hu, Zhenchang Xing, Mark Staples, Xiwei Xu, Qinghua Lu, Aaron Quigley

To the best of our knowledge, this is the first empirical study aimed at identifying the implications of AI adoption in language tests from a test-taker perspective.

Fairness

Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions

no code implementations • 8 Jul 2023 • Dawen Zhang, Pamela Finckenberg-Broman, Thong Hoang, Shidong Pan, Zhenchang Xing, Mark Staples, Xiwei Xu

In this paper, we explore these challenges and provide our insights on how to implement technical solutions for the RTBF, including the use of differential privacy, machine unlearning, model editing, and prompt engineering.

Machine Unlearning Model Editing +1

To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods

no code implementations • 7 Feb 2023 • Dawen Zhang, Shidong Pan, Thong Hoang, Zhenchang Xing, Mark Staples, Xiwei Xu, Lina Yao, Qinghua Lu, Liming Zhu

The right to be forgotten (RTBF) is motivated by the desire of people not to be perpetually disadvantaged by their past deeds.

Ethics Fairness +1