  Naively trained AI models can be heavily biased. This can be particularly
problematic when the biases involve legally or morally protected attributes
such as ethnic background, age or gender. Existing solutions to this problem
come at the cost of extra computation, unstable adversarial optimisation or
have losses on the feature space structure that are disconnected from fairness
measures and only loosely generalise to fairness. In this work we propose a
differentiable approximation of the variance of demographics, a metric that can
be used to measure the bias, or unfairness, in an AI model. Our approximation
can be optimised alongside the regular training objective which eliminates the
need for any extra models during training and directly improves the fairness of
the regularised models. We demonstrate that our approach improves the fairness
of AI models in varied task and dataset scenarios, whilst still maintaining a
high level of classification accuracy. Code is available at
https://bitbucket.org/nelliottrosa/base_fairness.
