  We introduce the BriarPatch, a pixel-space intervention that obscures
sensitive attributes from representations encoded in pre-trained classifiers.
The patches encourage internal model representations not to encode sensitive
information, which has the effect of pushing downstream predictors towards
exhibiting demographic parity with respect to the sensitive information. The
net result is that these BriarPatches provide an intervention mechanism
available at user level, and complements prior research on fair representations
that were previously only applicable by model developers and ML experts.
