  As they have a vital effect on social decision-making, AI algorithms should
be not only accurate but also fair. Among various algorithms for fairness AI,
learning fair representation (LFR), whose goal is to find a fair representation
with respect to sensitive variables such as gender and race, has received much
attention. For LFR, the adversarial training scheme is popularly employed as is
done in the generative adversarial network type algorithms. The choice of a
discriminator, however, is done heuristically without justification. In this
paper, we propose a new adversarial training scheme for LFR, where the integral
probability metric (IPM) with a specific parametric family of discriminators is
used. The most notable result of the proposed LFR algorithm is its theoretical
guarantee about the fairness of the final prediction model, which has not been
considered yet. That is, we derive theoretical relations between the fairness
of representation and the fairness of the prediction model built on the top of
the representation (i.e., using the representation as the input). Moreover, by
numerical experiments, we show that our proposed LFR algorithm is
computationally lighter and more stable, and the final prediction model is
competitive or superior to other LFR algorithms using more complex
discriminators.
