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Galaxy mergers are hugely important in our current dark matter cosmology.These powerful events cause the disruption of the merging galaxies, pushing thegas, stars and dust of the galaxies resulting in changes to morphologies. Thisdisruption can also cause more extreme events inside the galaxies: periods ofextreme star formation rates and the rapid increase in active galactic nucleiactivity. Hence, to better understand what goes on in these rare events, weneed to be able to identify statistically large samples.  In the last few years, the growth of artificial intelligence techniques hasseen application to identifying galaxy mergers. These techniques have shown tobe highly accurate and their application has grown beyond academic studies of``can we?'' to deeper scientific use. However, these classifications are notwithout their problems.  In this proceedings, we will explore how galaxy merger classification can beimproved by adding pre-extracted galaxy morphologies alongside the traditionalimaging data. This demonstrates that current neural networks are not extractingall the information from the images they are given. It will also explore howthe resulting samples of rare objects could be highly contaminated. This has aknock on impact on the upcoming large scale surveys like Euclid and Rubin-LSST.

Pitfalls of AI classification of rare objects: Galaxy Mergers