Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models

Ki Hyun Tae, a Ph.D. student of Prof. Steven Euijong Whang in the EE department, proposed a selective data acquisition framework for accurate and fair machine learning models.

As machine learning becomes widespread in our everyday lives, making AI more responsible is becoming critical. Beyond high accuracy of AI, the key objectives of responsible AI include fairness, robustness, explainability, and more. In particular, companies including Google, Microsoft, and IBM are emphasizing responsible AI.

Among the objectives, this work focuses on model fairness. Based on the key insight that the root cause of unfairness is in biased training data, Ki Hyun proposed Slice Tuner, a selective data acquisition framework that optimizes both model accuracy and fairness. Slice Tuner efficiently and reliably manages learning curves, which are used to estimate model accuracy given more data, and utilizes them to provide the best data acquisition strategy for training an accurate and fair model.

The research team believes that Slice Tuner is an important first step towards realizing responsible AI starting from data collection. This work was presented at ACM SIGMOD (International Conference on Management of Data) 2021, a top Database conference.

For more details, please refer to the links below.

 

Figure 1. Slice Tuner architecture

https://arxiv.org/abs/2003.04549

https://docs.google.com/presentation/d/1thnn2rEvTtcCbJc8s3TnHQ2IEDBsZOe6...

https://youtu.be/QYEhURcd4u4?list=PL3xUNnH4TdbsfndCMn02BqAAgGB0z7cwq

 

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