Proceed with caution: how, and how not, to use machine learning to probe cosmology

Machine learning bears the potential to revolutionize observational cosmology, especially with the big data anticipated of upcoming surveys that aim to scrape the dregs of the uncertainty-dominated sky. However, the validity of analyses utilizing such techniques hinges on first solving a number of open and open-ended methodological questions. I present a multifaceted case study of the rewards and risks of machine learning in the context of galaxy survey cosmology with broad-band photometric redshifts by reviewing the results of Schmidt, Malz, & Soo, et al. (2020) and Malz & Hogg (2021). I identify prerequisites to the usage of probabilistic data products of machine learning to cosmological problems and outline priorities for future directions of data-driven astronomical research, highlighting in particular a need for methods yielding likelihoods of data conditioned on parameters rather than posteriors of parameters conditioned on data.