Professors Steven Euijong Whang and Changho Suh’s research team in the School of Electrical Engineering has developed a new batch selection technique for fair artificial intelligence (AI) systems. The research was led by Ph.D. student Yuji Roh (advisor: Steven Euijong Whang) and was conducted in collaboration with Professor Kangwook Lee from the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison.
AI technologies are now widespread and influence everyday lives of humans. Unfortunately, researchers have recently observed that machine learning models may discriminate against specific demographics or individuals. As a result, there is a growing social consensus that AI systems need to be fair.
The research team proposes FairBatch, a new batch selection technique for building fair machine learning models. Existing fair training algorithms require significant non-trivial modifications either in the training data or model architecture. In contrast, FairBatch effectively achieves high accuracy and fairness with only a single-line change of code in the batch selection, which enables FairBatch to be easily deployed in various applications. FairBatch’s key approach is solving a bi-level optimization for jointly achieving accuracy and fairness.
This research was presented at the International Conference for Learning Representations (ICLR) 2021, a top machine learning conference. More details are in the links below.
Figure 1. A scenario that shows how FairBatch adaptively adjusts batch ratios in model training for fairness.
Figure 2. PyTorch code for model training where FairBatch is used for batch selection. Only a single-line code change is required to replace an existing sampler with FairBatch, marked in blue.
Title: FairBatch: Batch Selection for Model Fairness
Authors: Yuji Roh (KAIST EE), Kangwook Lee (Wisconsin-Madison Electrical & Computer Engineering), Steven Euijong Whang (KAIST EE), and Changho Suh (KAIST EE)