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Testing whether data breaks symmetries of interest can be important to manyfields. This paper describes a simple way that machine learning algorithms(whose outputs have been appropriately symmetrised) can be used to detectsymmetry breaking. The original motivation for the paper was an importantquestion in Particle Physics: "Is parity violated at the LHC in some way thatno-one has anticipated?" and so we illustrate the main idea with an examplestrongly related to that question. However, in order that the key ideas beaccessible to readers who are not particle physicists but who are interestingin symmetry breaking, we choose to illustrate the method/approach with a 'toy'example which places a simple discrete source of symmetry breaking (thehandedness of human handwriting) within a idealised particle-physics-likecontext. Readers interested in seeing extensions to continuous symmetries,non-ideal environments or more realistic particle-physics contexts are providedwith links to separate papers which delve into such details.

Using unsupervised learning to detect broken symmetries, with relevance  to searches for parity violation in nature. (Previously: "Stressed GANs snag  desserts")