Don't Judge Me by My Face：An Indirect Adversarial Approach to Remove敏感信息From Multimodal Neural Representation in Asynchronous Job Video Interviews
Don't Judge Me by My Face : An Indirect Adversarial Approach to Remove Sensitive Information From Multimodal Neural Representation in Asynchronous Job Video Interviews
摘要：
用于自动分析求职面试视频的机器学习的SE最近引起了越来越多的兴趣。尽管关于候选人的性别或种族等敏感信息的输出公平，但目前的方法很少提供无偏见决策的证据，或者没有使用敏感信息。最近，对抗方法已被证明可以有效地从神经网络的潜在表示中去除敏感信息。然而，这些方法依赖于使用明确标记的受保护变量（例如性别），在某些国家（例如法国）的招募背景下无法收集这些变量。在这篇文章中，我们提出了一种新的对抗性方法，从神经网络的潜在表示中删除敏感信息，而不需要收集任何敏感变量。只使用几帧面试，我们训练我们的模型，使其无法在模型的内层中找到与工作面试相关的候选人的面孔。这反过来又允许我们从这些层中删除相关的私人信息。将我们的方法与带有性别和种族注释的公共数据集上的标准基线进行比较，我们表明它有效地从主网络中删除了敏感信息。此外，据我们所知，这是对抗技术在视频面试中获得多模态公平表示的第一次应用。总之，我们的贡献旨在提高即将推出的自动系统处理求职面试视频的公平性，以实现就业选择的平等。
英文摘要：
se of machine learning for automatic analysis of job interview videos has recently seen increased interest. Despite claims of fair output regarding sensitive information such as gender or ethnicity of the candidates, the current approaches rarely provide proof of unbiased decision-making, or that sensitive information is not used. Recently, adversarial methods have been proved to effectively remove sensitive information from the latent representation of neural networks. However, these methods rely on the use of explicitly labeled protected variables (e.g. gender), which cannot be collected in the context of recruiting in some countries (e.g. France). In this article, we propose a new adversarial approach to remove sensitive information from the latent representation of neural networks without the need to collect any sensitive variable. Using only a few frames of the interview, we train our model to not be able to find the face of the candidate related to the job interview in the inner layers of the model. This, in turn, allows us to remove relevant private information from these layers. Comparing our approach to a standard baseline on a public dataset with gender and ethnicity annotations, we show that it effectively removes sensitive information from the main network. Moreover, to the best of our knowledge, this is the first application of adversarial techniques for obtaining a multimodal fair representation in the context of video job interviews. In summary, our contributions aim at improving fairness of the upcoming automatic systems processing videos of job interviews for equality in job selection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
