Howe8 addressed an AI community that at that point “rarely publish[ed] performance evaluations” of their proposed algorithms and instead only described the systems. They suggested establishing sensible metrics for quantifying progress, and analyzing the following: “Why does it work?” “Under what circumstances won’t it work?” and “Have the design decisions been justified?”—questions that continue to resonate today.

Finally, in 2009 Armstrong et al. 1 discussed the empirical rigor of in-formation-retrieval research, noting a tendency of papers to compare against the same weak baselines, producing a long series of improvements that did not accumulate to meaningful gains.

In other fields, an unchecked decline in scholarship has led to crisis. A landmark study in 2015 suggested a significant portion of findings in the psychology literature may not be reproducible. 33 In a few historical cases, enthusiasm paired with undisciplined scholarship led entire communities down blind alleys. For example, following the discovery of X-rays, a related discipline on N-rays emerged before it was eventually debunked. 32

Concluding Remarks

The reader might rightly suggest these problems are self-correcting. We agree. However, the community self-corrects precisely through recurring debate about what constitutes reasonable standards for scholarship. We hope that this paper contributes constructively to the discussion.

Acknowledgments

We thank Asya Bergal, Kyunghyun Cho, Moustapha Cisse, Daniel Dewey, Danny Hernandez, Charles Elkan, Ian Goodfellow, Moritz Hardt, Tatsunori Hashimoto, Sergey Ioffe, Sham Kakade, David Kale, Holden Karnofsky, Pang Wei Koh, Lisha Li, Percy Liang, Julian McAuley, Robert Nishihara, Noah Smith, Balakrishnan “Murali” Narayanaswamy, Ali Rahimi, Christopher Ré, and Byron Wallace. We also thank the ICML Debates organizers.

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Zachary C. Lipton is an assistant professor at Carnegie Mellon University in the Tepper School of Business with appointments in the Machine Learning Department and the Heinz School of Public Policy. He also collaborates with Amazon, where he helped to grow AWS’ Amazon AI team and contributed to the Apache MXNet deep learning framework. Find him at zacklipton.com, Twitter @zacharylipton, or GitHub @zackchase.

Jacob Steinhardt will be joining UC Berkeley as an assistant professor of statistics. He is a technical advisor for the Open Philanthropy Project and has collaborated with policy researchers to understand and avoid potential misuses of machine learning.

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