计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (11): 122-131.doi: 10.11896/jsjkx.220900169

• 数据库&大数据&数据科学 • 上一篇    下一篇

NeuronSup:基于偏见神经元抑制的深度模型去偏方法

倪洪杰1, 刘嘉威1, 郑海斌1,2, 陈奕芃1, 陈晋音1,2   

  1. 1 浙江工业大学信息工程学院 杭州 310023
    2 浙江工业大学网络空间安全研究院 杭州 310023
  • 收稿日期:2022-09-18 修回日期:2023-01-06 出版日期:2023-11-15 发布日期:2023-11-06
  • 通讯作者: 陈晋音(chenjinyin@zjut.edu.cn)
  • 作者简介:(zdfynhj@zjut.edu.cn)
  • 基金资助:
    国家自然科学基金(62072406);浙江省自然科学基金(LDQ23F020001);信息系统安全技术重点实验室基金(61421110502);浙江省重点研发计划(2021C01117);2020年工业互联网创新发展工程项目(TC200H01V);浙江省“万人计划”科技创新领军人才项目(2020R52011)

NeuronSup:Deep Model Debiasing Based on Bias Neuron Suppression

NI Hongjie1, LIU Jiawei1, ZHENG Haibin1,2, CHEN Yipeng1, CHEN Jinyin1,2   

  1. 1 College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023 China
    2 Institute of Cyberspace Security,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2022-09-18 Revised:2023-01-06 Online:2023-11-15 Published:2023-11-06
  • About author:NI Hongjie,born in 1978,Ph.D,Ph.D supervisor.His main research interests include artificial intelligence security and data mining.CHEN Jinyin,born in 1982,Ph.D,professor.Her main research interests include artificial intelligence security,graph data mining and evolutionary computing.
  • Supported by:
    National Natural Science Foundation of China(62072406),Natural Science Foundation of Zhejiang Province,China(LDQ23F020001),Chinese National Key Laboratory of Science and Technology on Information System Security(61421110502),Key R&D Projects in Zhejiang Province(2021C01117),2020 Industrial Internet Innovation Development Project(TC200H01V) and “Ten Thousand Talents Program” in Zhejiang Province(2020R52011).

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摘要： 随着深度学习的广泛应用,研究者在关注模型分类性能的同时,还需要关注模型的决策是否公平可信。存在决策偏见的深度模型会造成极大的负面影响,因此如何维持深度模型的分类正确率,同时提高模型的决策公平至关重要。目前已有工作提出了较多方法,用于改善模型的个体公平,但是这些方法仍然在去偏效果、去偏后模型可用性、去偏效率等方面存在缺陷。为此,文中分析了深度模型存在个体偏见时神经元异常激活现象,提出了一种基于偏见神经元抑制的模型去偏方法NeuronSup,具有显著降低个体偏见、对主任务性能影响小、时间复杂度低等优势。具体而言,首先根据深度模型部分神经元由于个体偏见而产生异常激活的现象提出了偏见神经元的概念。然后,利用歧视样本对查找深度模型中的偏见神经元,通过抑制偏见神经元的异常激活大幅降低深度模型的个体偏见,并且根据每个神经元的最大权重边确定主任务性能神经元,通过保持深度模型的主任务性能神经元参数不变,来减小去偏操作对深度模型分类性能造成的影响。因为 NeuronSup只对深度模型中的特定神经元进行去偏操作,所以时间复杂度更低,效率更高。最后,在3个真实数据集的6种敏感属性上开展去偏实验,与5种对比算法相比,NeuronSup将个体公平指标THEMIS降低了50%以上,同时使去偏操作对深度模型分类准确率的影响降低到3%以内,验证了NeuronSup在保证深度模型分类能力的情况下降低个体偏见的有效性。

关键词: 个体公平, 深度学习, 偏见神经元, 模型去偏

Abstract: With the wide application of deep learning,researchers not only focus on the classification performance of the model,but also need to pay attention to whether the decision of the model is fair and credible.A deep learning model with decision bias may cause great negative effects,so how to maintain the classification accuracy and improve the decision fairness of the model is very important.At present,many methods have been proposed to improve the individual fairness of the model,but there are still shortcomings in the debiasing effect,the availability of the debiased model,and the debiasing efficiency.To this end,this paper analyzes the abnormal activation of neurons when there is individual bias in the deep model,and proposes a model debiasing me-thod NeuronSup based on the inhibition of biased neurons,which has the advantages of significantly reducing individual bias,less impact on the performance of the main task,and low time complexity.To be specific,the concept of bias neuron is first proposed based on the phenomenon that some neurons in the deep model are abnormally activated due to individual bias.Then,the bias neurons are found by using discrimination samples,and the individual bias of the deep model is greatly reduced by suppressing the abnormal activation of bias neurons.And the main task performance neurons are determined according to the maximum weight edge of each neuron.By keeping the main task performance neuron parameters of the deep model unchanged,the influence of debiasing operation on the classification performance of the deep model could be reduced.Because NeuronSup only debiases specific neurons in the deep model,the time complexity is lower and the efficiency is higher.Finally,debiasing experiments on three real datasets with six sensitive attributes,compared with five contrasting algorithms,NeuronSup reduces the individual fairness index THEMIS more than 50%,and at the same time,the impact of the debiasing operation on the classification accuracy of the deep model is reduced to less than 3%,which verifies the effectiveness of NeuronSup in reducing individual bias while ensuring the classification ability of deep model.

Key words: Individual fairness, Deep learning, Bias neurons, Model debiasing

中图分类号: 

  • TP391
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