ISSTA 2024
Mon 16 - Fri 20 September 2024 Vienna, Austria
co-located with ISSTA/ECOOP 2024
Wed 18 Sep 2024 13:30 - 13:50 at EI 7 - Fairness and Safety of Neural Networks Chair(s): Jingyi Wang

This paper investigates neuron dropout as a post-processing bias mitigation method for deep neural networks (DNNs). Neural-driven software solutions are increasingly applied in socially critical domains with significant fairness implications. While DNNs are exceptional at learning statistical patterns from data, they may encode and amplify historical biases. Existing bias mitigation algorithms often require modifying the input dataset or the learning algorithms. We posit that prevalent dropout methods may be an effective and less intrusive approach to improve fairness of pre-trained DNNs during inference. However, finding the ideal set of neurons to drop is a combinatorial problem.

We propose NeuFair, a family of post-processing randomized algorithms that mitigate unfairness in pre-trained DNNs via dropouts during inference. Our randomized search is guided by an objective to minimize discrimination while maintaining the model’s utility. We show that NeuFair is efficient and effective in improving fairness (up to 69%) with minimal or no model performance degradation. We provide intuitive explanations of these phenomena and carefully examine the influence of various hyperparameters of NeuFair on the results. Finally, we empirically and conceptually compare NeuFair to different state-of-the-art bias mitigators.

Wed 18 Sep

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

13:30 - 14:50
Fairness and Safety of Neural NetworksTechnical Papers at EI 7
Chair(s): Jingyi Wang Zhejiang University
13:30
20m
Talk
NeuFair: Neural Network Fairness Repair with Dropout
Technical Papers
Vishnu Asutosh Dasu Pennsylvania State University, Ashish Kumar Pennsylvania State University, Saeid Tizpaz-Niari University of Texas at El Paso, Gang (Gary) Tan Pennsylvania State University
DOI
13:50
20m
Talk
A Large-Scale Empirical Study on Improving the Fairness of Image Classification Models
Technical Papers
Junjie Yang College of Intelligence and Computing, Tianjin University, Jiajun Jiang Tianjin University, Zeyu Sun Institute of Software at Chinese Academy of Sciences, Junjie Chen Tianjin University
DOI
14:10
20m
Talk
Efficient DNN-Powered Software with Fair Sparse Models
Technical Papers
Xuanqi Gao Xi’an Jiaotong University, Weipeng Jiang Xi’an Jiaotong University, Juan Zhai University of Massachusetts at Amherst, Shiqing Ma University of Massachusetts at Amherst, Xiaoyu Zhang Xi’an Jiaotong University, Chao Shen Xi’an Jiaotong University
DOI Pre-print
14:30
20m
Talk
Synthesizing Boxes Preconditions for Deep Neural Networks
Technical Papers
Zengyu Liu National University of Defense Technology, Liqian Chen National University of Defense Technology, Wanwei Liu National University of Defense Technology, Ji Wang National University of Defense Technology
DOI

Information for Participants