In Defense of the Paper

TL;DR

This paper argues that the core issues in machine learning publication are due to systemic incentive misalignments rather than the paper format or workflow, advocating for addressing these root causes.

Contribution

It challenges the notion that changing publication formats will fix systemic problems, emphasizing the need to realign incentives in research and publishing.

Findings

Systemic incentives drive publication accessibility issues.

Replacing paper formats alone won't solve underlying problems.

Addressing research incentives is key to improving the ecosystem.

Abstract

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.