  Testing whether data breaks symmetries of interest can be important to many
fields. This paper describes a simple way that machine learning algorithms
(whose outputs have been appropriately symmetrised) can be used to detect
symmetry breaking. The original motivation for the paper was an important
question in Particle Physics: "Is parity violated at the LHC in some way that
no-one has anticipated?" and so we illustrate the main idea with an example
strongly related to that question. However, in order that the key ideas be
accessible to readers who are not particle physicists but who are interesting
in symmetry breaking, we choose to illustrate the method/approach with a 'toy'
example which places a simple discrete source of symmetry breaking (the
handedness of human handwriting) within a idealised particle-physics-like
context. Readers interested in seeing extensions to continuous symmetries,
non-ideal environments or more realistic particle-physics contexts are provided
with links to separate papers which delve into such details.
