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.

翻译：我们警告一种常见但不完整的机器学习（ML）实证研究理解，这种理解导致结果不可复现、使研究发现不可靠，并威胁到该领域的进展。为应对这一令人担忧的局面，我们呼吁提高对通过实验获取知识的多种方式及其认知局限性的认识。具体而言，我们认为当前大多数实证ML研究被构建为验证性研究，而实际上更应被视为探索性研究。