With the swift advancement of deep learning, state-of-the-art algorithms have been utilized in various social situations. Nonetheless, some algorithms have been discovered to exhibit biases and provide unequal results. The current debiasing methods face challenges such as poor utilization of data or intricate training requirements. In this work, we found that the backdoor attack can construct an artificial bias similar to the model bias derived in standard training. Considering the strong adjustability of backdoor triggers, we are motivated to mitigate the model bias by carefully designing reverse artificial bias created from backdoor attack. Based on this, we propose a backdoor debiasing framework based on knowledge distillation, which effectively reduces the model bias from original data and minimizes security risks from the backdoor attack. The proposed solution is validated on both image and structured datasets, showing promising results. This work advances the understanding of backdoor attacks and highlights its potential for beneficial applications. The code for the study can be found at \url{https://anonymous.4open.science/r/DwB-BC07/}.

翻译：随着深度学习的快速发展，最先进的算法已被应用于各类社会场景。然而，部分算法被发现存在偏见并导致不平等的结果。当前的去偏方法面临数据利用率低或训练要求复杂等挑战。本研究发现，后门攻击能够构建与标准训练中产生的模型偏见相似的人为偏差。基于后门触发器的强可调性，我们通过精心设计由后门攻击产生的反向人为偏差来缓解模型偏见。据此，我们提出一种基于知识蒸馏的后门去偏框架，该框架能有效降低源自原始数据的模型偏见，并最大程度减少后门攻击带来的安全风险。所提出的方法在图像和结构化数据集上均得到验证，展现了良好的效果。本研究深化了对后门攻击的理解，并凸显其在有益应用中的潜力。研究代码可在 \url{https://anonymous.4open.science/r/DwB-BC07/} 获取。