  The machine learning publication process is broken, of that there can be no
doubt. Many of these flaws are attributed to the current workflow: LaTeX to PDF
to reviewers to camera ready PDF. This has understandably resulted in the
desire for new forms of publications; ones that can increase inclusively,
accessibility and pedagogical strength. However, this venture fails to address
the origins of these inadequacies in the contemporary paper workflow. The
paper, being the basic unit of academic research, is merely how problems in the
publication and research ecosystem manifest; but is not itself responsible for
them. Not only will simply replacing or augmenting papers with different
formats not fix existing problems; when used as a band-aid without systemic
changes, will likely exacerbate the existing inequities. In this work, we argue
that the root cause of hindrances in the accessibility of machine learning
research lies not in the paper workflow but within the misaligned incentives
behind the publishing and research processes. We discuss these problems and
argue that the paper is the optimal workflow. We also highlight some potential
solutions for the incentivization problems.
