The article reviews the attention paid to an important issue in machine learning research, but the problem raised can be generalized to other fields within computer science and informatics.

Machine learning and the research area known as artificial intelligence (AI) is a fashionable, fast-growing research field that is prone to business interests. The authors discuss the problems related to the scientific content of papers, as well as how the presented ideas and measurements can be evaluated by reviewers and the scientific community. The authors emphasize that scientific rigor has developed essentially over the past decade; however, some phenomena should be dealt with. A summary of the raised issues include: the source of demonstrated empirical advances should be clearly and experimentally underpinned; requirements for mathematical formalism are excessively demanding, sometimes blurring the obscure idea that leads to “mathiness”; and the use of language that misleads readers by creating connotations without profound grounding.

The article highlights that other scientific disciplines must deal with similar problems, and is a recurring phenomenon even in AI. It concludes that self-correction through the scientific community can happen via recurring debates. Keeping the problem on agenda--“what constitutes reasonable standards for scholarship”--is an important task. My personal experiences with reviewing scientific papers from diverse areas in computing buttress the opinions expressed in this article. We can hope that the evolution of the scientific discipline might define the appropriate standards that are to be applied.