We warn against a common but incomplete understanding of empirical research
in machine learning (ML) that leads to non-replicable results, makes findings
unreliable, and threatens to undermine progress in the field. To overcome this
alarming situation, we call for more awareness of the plurality of ways of
gaining knowledge experimentally but also of some epistemic limitations. In
particular, we argue most current empirical ML research is fashioned as
confirmatory research while it should rather be considered exploratory.

我们警告人们对机器学习领域的经验研究常常存在着一种不完整的理解，这导致实验结果不可复制、发现不可靠，进而威胁到该领域的进展。为了克服这种令人担忧的情况，我们呼吁更多地意识到获得知识的多样性方式以及一些认识论上的限制。特别是，我们认为当前大部分经验机器学习研究实质上是确认性研究，而应该视为探索性研究。