We warn against a common but incomplete understanding of empirical research in machine learning 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 machine learning research is fashioned as confirmatory research while it should rather be considered exploratory.

翻译：我们警示当前对机器学习实证研究的一种普遍但不完整的理解，这种理解导致结果不可复现、研究发现不可靠，并可能危及该领域的进展。为克服这一令人担忧的状况，我们呼吁学界应更充分地认识到实验性知识获取方式的多样性及其认知局限性。特别需要指出的是，我们认为当前大多数机器学习实证研究被设计为验证性研究，而实际上更应被视为探索性研究。