5 citations · 8 across the 3 of their papers we have counts for
6 papers
Sample Selection for Fair and Robust Training
Yuji Roh, Kangwook Lee, Steven Euijong Whang +1
Fairness and robustness are critical elements of Trustworthy AI that need to be addressed together. Fairness is about learning an unbiased model while robustness is about learning…
Responsible AI Challenges in End-to-end Machine Learning
Steven Euijong Whang, Ki Hyun Tae, Yuji Roh +1
Responsible AI is becoming critical as AI is widely used in our everyday lives. Many companies that deploy AI publicly state that when training a model, we not only need to improve…
FairBatch: Batch Selection for Model Fairness
Yuji Roh, Kangwook Lee, Steven Euijong Whang +1
Training a fair machine learning model is essential to prevent demographic disparity. Existing techniques for improving model fairness require broad changes in either data preproce…
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
Yuji Roh, Kangwook Lee, Steven Euijong Whang +1
Trustworthy AI is a critical issue in machine learning where, in addition to training a model that is accurate, one must consider both fair and robust training in the presence of d…
Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach
Ki Hyun Tae, Yuji Roh, Young Hun Oh +2
The wide use of machine learning is fundamentally changing the software development paradigm (a.k.a. Software 2.0) where data becomes a first-class citizen, on par with code. As ma…
A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective
Yuji Roh, Geon Heo, Steven Euijong Whang
Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a…