  Non-Standard-Model parity violation may be occurring in LHC collisions. Any
such violation would go unseen, however, as searches are for it are not
currently performed. One barrier to searches for parity violation is the lack
of model-independent methods sensitive to all of its forms. We remove this
barrier by demonstrating an effective and model-independent way to search for
parity-violating physics at the LHC. The method is data-driven and makes no
reference to any particular parity-violating model. Instead, it inspects data
to construct sensitive parity-odd event variables (using machine learning
tools), and uses these variables to test for parity asymmetry in independent
data. We demonstrate the efficacy of this method by testing it on data
simulated from the Standard Model and from a non-standard parity-violating
model. This result enables the possibility of investigating a variety of
previously unexplored forms of parity violation in particle physics. Data and
software are shared at https://zenodo.org/record/6827724
