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