Deep neural networks are efficient at learning the data distribution if it is
sufficiently sampled. However, they can be strongly biased by non-relevant
factors implicitly incorporated in the training data. These include operational
biases, such as ineffective or uneven data sampling, but also ethical concerns,
as the social biases are implicitly present\textemdash even inadvertently, in
the training data or explicitly defined in unfair training schedules. In tasks
having impact on human processes, the learning of social biases may produce
discriminatory, unethical and untrustworthy consequences. It is often assumed
that social biases stem from supervised learning on labelled data, and thus,
Self-Supervised Learning (SSL) wrongly appears as an efficient and bias-free
solution, as it does not require labelled data. However, it was recently proven
that a popular SSL method also incorporates biases. In this paper, we study the
biases of a varied set of SSL visual models, trained using ImageNet data, using
a method and dataset designed by psychological experts to measure social
biases. We show that there is a correlation between the type of the SSL model
and the number of biases that it incorporates. Furthermore, the results also
suggest that this number does not strictly depend on the model's accuracy and
changes throughout the network. Finally, we conclude that a careful SSL model
selection process can reduce the number of social biases in the deployed model,
whilst keeping high performance.

本文研究 Self-Supervised Learning (SSL) 方法在使用 ImageNet 数据集进行训练时是否存在社会偏见的问题，并探讨了不同类型的 SSL 模型与所包含偏见的关系，同时认为在模型选择过程中可以减少社会偏见的数量，同时保持高性能。