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.

中文翻译：

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捍卫文件

机器学习的发布过程已被打破，这是毫无疑问的。这些缺陷中的许多缺陷都归因于当前的工作流程：从LaTeX到PDF到审阅者到准备好相机的PDF。可以理解的是，这导致了对新形式出版物的需求。可以包括所有方面的内容，可访问性和教学实力。但是，这种尝试无法解决当代纸张工作流程中这些不足的根源。该论文是学术研究的基本单元，仅是出版物和研究生态系统中问题的表现方式。但本身不对他们负责。仅仅用不同格式替换或增加纸张不仅不能解决现有问题，而且还可以解决许多问题。当用作没有系统变化的创可贴时，可能会加剧现有的不平等现象。在这项工作中，我们认为，机器学习研究可及性障碍的根本原因不在于论文工作流，而在于出版和研究过程背后的不对劲动机。我们讨论了这些问题，并认为本文是最佳的工作流程。我们还将重点介绍一些针对激励问题的潜在解决方案。