in a submitted paper.

Misaligned incentives. Reviewers are not alone in providing poor incentives for authors. As ML research garners increased media attention and ML startups become commonplace, to some degree incentives are provided by the press (“What will they write about?”) and by investors (“What will they invest in?”). The media provides incentives for some of these trends.

Anthropomorphic descriptions of ML algorithms provide fodder for popular coverage. Take, for example, a 2014 article by Cade Metz in Wired, 28 that characterized an autoencoder as a “simulated brain.” Hints of human-level performance tend to be sensationalized in newspaper coverage— for example, an article in the New York Times by John Markoff described a deep-learning image-captioning system as “mimicking human levels of understanding.” 25

Investors, too, have shown a strong appetite for AI research, funding startups sometimes on the basis of a single paper. In my (Lipton) experience working with investors, they are sometimes attracted to startups whose research has received media coverage, a dynamic that attaches financial incentives to media attention. Note that recent interest in chatbot startups co-occurred with anthropomorphic descriptions of dialogue systems and reinforcement learners both in papers and in the media, although it may be difficult to determine whether the lapses in scholarship caused the interest of investors or vice versa.

Suggestions. Suppose we are to intervene to counter these trends, then how? Besides merely suggesting that each author abstain from these patterns, what can we do as a community to raise the level of experimental practice, exposition, and theory? And how can we more readily distill the knowledge of the community and disabuse researchers and the wider public of misconceptions? What follows are a number of preliminary suggestions based on personal experiences and impressions.

For Authors, Publishers,
and Reviewers

We encourage authors to ask “What
worked?” and “Why?” rather than just
“How well?” Except in extraordinary
a consequence, even papers that ap-
pear to be in dialogue with each other
may have different concepts in mind.

As another example, generalization has both a specific technical meaning (generalizing from training to testing) and a more colloquial meaning that is closer to the notion of transfer (generalizing from one population to another) or of external validity (generalizing from an experimental setting to the real world). Conflating these notions leads to overestimating the capabilities of current systems.

Suggestive definitions and overloaded terminology can contribute to the creation of new suitcase words. In the fairness literature, where legal, philosophical, and statistical language are often overloaded, terms such as bias become suitcase words that must be subsequently unpacked.

In common speech and as aspirational terms, suitcase words can serve a useful purpose. Sometimes a suitcase word might reflect an overarching aspiration that unites the various meanings. For example, artificial intelligence might be well suited as an aspirational name to organize an academic department. On the other hand, using suitcase words in technical arguments can lead to confusion. For example, in his 2017 book, Superintelligence, 3 Nick Bostrom wrote an equation (Box 4) involving the terms intelligence and optimization power, implicitly assuming these suitcase words can be quantified with a one-dimensional scalar.

Speculation on Causes
Behind the Trends

Do the patterns mentioned here represent a trend, and if so, what are the underlying causes? We speculate that these patterns are on the rise and suspect several possible causal factors: complacency in the face of progress, the rapid expansion of the community, the consequent thinness of the reviewer pool, and misaligned incentives of scholarship vs. short-term measures of success.

Complacency in the face of progress. The apparent rapid progress in ML has at times engendered an attitude that strong results excuse weak arguments.

Authors with strong results may feel li-
censed to insert arbitrary unsupported
stories (see “Explanation vs. Specula-
tion”) regarding the factors driving the
results; to omit experiments aimed at
disentangling those factors (see “Fail-
ure to Identify the Sources of Empiri-
cal Gains”); to adopt exaggerated ter-
minology (see “Misuse of Language”);
or to take less care to avoid mathiness
(see “Mathiness”).
At the same time, the single-round
nature of the reviewing process may
cause reviewers to feel they have no
choice but to accept papers with
strong quantitative findings. Indeed,
even if the paper is rejected, there is
no guarantee the flaws will be fixed or
even noticed in the next cycle, so re-
viewers may conclude that accepting a
flawed paper is the best option.

Growing pains. Since around 2012, the ML community has expanded rapidly because of increased popularity stemming from the success of deep-learning methods. While the rapid expansion of the community can be seen as a positive development, it can also have side effects.

To protect junior authors, we have preferentially referenced our own papers and those of established researchers. And certainly, experienced researchers exhibit these patterns. Newer researchers, however, may be even more susceptible. For example, authors unaware of previous terminology are more likely to misuse or redefine language (as discussed earlier).

Rapid growth can also thin the reviewer pool in two ways: by increasing the ratio of submitted papers to reviewers and by decreasing the fraction of experienced reviewers. Less-experienced reviewers may be more likely to demand architectural novelty, be fooled by spurious theorems, and let pass serious but subtle issues such as misuse of language, thus either incentivizing or enabling several of the trends described here. At the same time, experienced but overburdened reviewers may revert to a “checklist” mentality, rewarding more formulaic papers at the expense of more creative or intellectually ambitious work that might not fit a preconceived template. Moreover, overworked reviewers may not have enough time to fix—or even to notice—all of the issues