5 citations · 8 across the 5 of their papers we have counts for
5 papers
Falcon: Fair Active Learning using Multi-armed Bandits
Ki Hyun Tae, Hantian Zhang, Jaeyoung Park +2
Biased data can lead to unfair machine learning models, highlighting the importance of embedding fairness at the beginning of data analysis, particularly during dataset curation an…
iFlipper: Label Flipping for Individual Fairness
Hantian Zhang, Ki Hyun Tae, Jaeyoung Park +2
As machine learning becomes prevalent, mitigating any unfairness present in the training data becomes critical. Among the various notions of fairness, this paper focuses on the wel…
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…
Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models
Ki Hyun Tae, Steven Euijong Whang
As machine learning becomes democratized in the era of Software 2.0, a serious bottleneck is acquiring enough data to ensure accurate and fair models. Recent techniques including c…
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…