  Fair Representation Learning (FRL) is a broad set of techniques, mostly based
on neural networks, that seeks to learn new representations of data in which
sensitive or undesired information has been removed. Methodologically, FRL was
pioneered by Richard Zemel et al. about ten years ago. The basic concepts,
objectives and evaluation strategies for FRL methodologies remain unchanged to
this day. In this paper, we look back at the first ten years of FRL by i)
revisiting its theoretical standing in light of recent work in deep learning
theory that shows the hardness of removing information in neural network
representations and ii) presenting the results of a massive experimentation
(225.000 model fits and 110.000 AutoML fits) we conducted with the objective of
improving on the common evaluation scenario for FRL. More specifically, we use
automated machine learning (AutoML) to adversarially "mine" sensitive
information from supposedly fair representations. Our theoretical and
experimental analysis suggests that deterministic, unquantized FRL
methodologies have serious issues in removing sensitive information, which is
especially troubling as they might seem "fair" at first glance.
