  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.
