Academics share machine-learning research freely. Taxpayers should not have to pay twice to read our findings
Budding authors face a minefield when it comes to publishing their work. For a large fee, as much as $3,000, they can make their work available to anyone who wants to read it. Or they can avoid the fee and have readers pay the publisher instead. Often it is libraries that foot this bill through expensive annual subscriptions. This is not the plenty of wannabe fiction writers, it’s the business of academic publishing.
More than 200 years ago, Giuseppe Piazzi, an isolated astronomer in Palermo, Sicily, discovered a dwarf planet. For him, publishing meant writing a letter to his friend Franz von Zach. Each month von Zach collated letters from astronomers across Europe and redistributed them. No internet for these guys: they found out about the latest discoveries from leatherbound volumes of letters called Monatliche Correspondenz . The hour it took to disseminate research hurled up its own problems: by the time Piazzi’s data were published, countries around the world had vanished in the sun’s glare.
It was a 23 -year-old reader in Gottingen who saved the day. Employing Kepler’s laws of planetary motion, Carl Friedrich Gauss calculated the locating of what we know today as Ceres. Gauss, who became Germany’s greatest mathematician, and Piazzi shared their learnings freely, but they accepted the need to pay for the work that von Zach undertook. This is the closed-access publishing model.
In my own field of machine learning, itself an academic descendant of Gauss’s pioneering run, modern data are no longer just planetary observations but medical images, spoken language, internet the documentation and more. The results are medical diagnosings, recommender systems, and whether driverless cars assure stop signs or not. Machine learning is the field that underpins the current revolution in artificial intelligence.
Machine learning is a young and technologically astute field. It does not have the historical traditions of other fields and its academics have insured no need for the closed-access publishing model. The community itself generated, collated, and reviewed the research it carried out. We used the internet to make new publications that were freely available and constructed no charge to authors. The era of subscriptions and leatherbound volumes seemed to be behind us.
The public already pays taxes that fund our research. Why should people get paid again to read the results? Colleagues in less well-funded universities also benefit. Makerere University in Kampala, Uganda, has as much access to the leading machine-learning research as Harvard or MIT. The ability to pay no longer determines the ability to play.
Machine learning has demonstrated that an academic field can not only survive, but thrive, without the involvement of commercial publishers. But this has not stopped traditional publishers from entering the market. Our success has caught their attention. Most lately, the publishing conglomerate Springer Nature announced a new periodical targeted at the community called Nature Machine Intelligence. The publisher now has 53 publications that bear the Nature name.
Should we be concerned? What would drive authors and readers towards a for-profit subscription publication when we already have an open model for sharing our ideas? Academic publishers have one card left to play: their brand. The diversity and quantity of academic research means that it is difficult for a researcher in one field to rate the work in another. Sometimes a journal’s brand is used as a proxy for quality. When academics look for promotion, having newspapers in a” brand-name publication” can be a big help. Nature is the Rolex of academic publishing. But in contrast to Rolex, whose staff are responsible for the innovation in its watches, Nature relies on academics to provide its content. We are the watchmakers, they are merely the distributors.
Many in our research community assure the Nature brand as a poor proxy for academic quality. We defy the intrusion of for-profit publishing into our field. As a result, at the time of writing, more than 3,000 researchers, including many resulting names in the field from both the enterprises and academia, have signed a statement refusing to submit, review or edit for this new journal. We ensure no role for closed access or author-fee publication in the future of machine-learning research. We believe the adoption of this new journal as an outlet of record for the machine-learning community would be a retrograde step.
Neil Lawrence is on leave of absence from the University of Sheffield and is working at Amazon. He is the founding editor of the freely available journal Proceedings of Machine Learning Research, which has to date published nearly 4,000 papers. The ideas in this article represent his personal sentiment .
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