Researchers often have trouble reproducing, or verifying, supposedly groundbreaking work described in scientific papers, raising questions about whether the findings in studies are genuine.
Over the past few years, they've been increasingly sounding alarms about this so-called reproducibility (or replication) crisis, concerned that scholars are routinely overstating their findings. The problem is particularly acute in the field of artificial intelligence, in which researchers have published a number of non-peer reviewed papers about topics like speech recognition and diagnosing medical conditions that others have been unable to replicate.
Now, a team from Northwestern University’s Kellogg School of Management and its Institute on Complex Systems has published a paper detailing a deep-learning system that it claims can figure out whether certain papers can be replicated or not. The paper, published Monday in the Proceedings of National Academy of Sciences of the United States of America journal, is noteworthy because it could help companies, universities, and other groups screen thousands of research papers while surfacing studies that are most likely to be reliable.
