Aarhus, Denmark -- 1 November 2019. Today, UNSILO has released the results of a survey on AI usage in academic publishing. How is it used? What factors impede its adoption? More than 3,000 new academic journal articles are published every day, and yet many of the tools for processing content through the academic workflow are frequently very manually based. The UNSILO survey, with publishers and other stakeholders in the scholarly workflow, was done between June and September 2019. The results were presented at a panel session at the Frankfurt Book Fair, chaired by Toby Green, former head of publishing at OECD.

Take-up of AI

The findings show a steady take-up of AI tools by publishers. Two-thirds of respondents are currently using at least one AI tool, and only 3% of respondents felt that AI could not benefit their activities in some way. Forty-five percent of respondents not currently using AI plan to introduce some AI tools within the coming twelve months. As for the perennial ‘build or buy' question, around one-third of publishers use only their own in-house resources. The remainder use external suppliers or a mixture of the two. How the publishers plan to expand their AI capability is interesting. The largest response was to expand the publisher's internal AI team, which suggests an emphasis on keeping skills internal to the organisation, rather than buying in external tools.

For publishers currently using AI tools, the biggest justification provided for AI tools was saving time (65%), suggesting that the current implementation of AI tools is based very much around tools to improve specific pain points in the academic workflow. By far the biggest application is to provide text analytics tools (over 40% of all implementations). The most widely used AI tools are machine learning and NLP (natural-language processing), with rule-based tools close behind. Linked data is used by less than 10% of respondents, and open linked data by even fewer. The largest single use of AI tools is to add and to curate metadata. Remarkably, over 40% of metadata is added by in-house staff, which suggests there is ample scope for automation.

Trust, bias and accuracy

For all the coverage in the media about questions of bias and privacy being topics of major concern, few of the respondents seemed to be taking action about these issues. Fewer than 10% of publishers check their AI tools for bias, and only 8% for privacy and compliance with GDPR. There is a paradox here; the two biggest reasons cited for not using AI tools more were "not enough time" and "uncertain quality of results". But fewer than 20% of respondents stated they were checking the AI tools they use for reliability and consistency.

Thomas Laursen, Chief Executive Officer at UNSILO, stated: "This survey confirms our experience with several academic publishers, who are both keen to get involved with AI and yet at the same time reluctant to relinquish human control over the academic workflow. We hope this survey will encourage more publishers to take the plunge with this transformational technology. It will be interesting to compare these results with the situation in a year's time, as more publishers learn from their experience and provide feedback."

About UNSILO

UNSILO ( www.unsilo.ai ), based in Aarhus, Denmark, is an artificial intelligence software company that develops advanced tools for text understanding and processing. UNSILO tools deliver dramatic workflow improvements by reducing processing time, while at the same time improving quality and accuracy. The UNSILO Document Enrichment service forms the basis of over 20 separate functional solutions and APIs for publishers, including identifying trending topics as they emerge, improving the quality of document abstracts, and locating relatedness,

including related content, similar journals or matching experts. UNSILO Classify is a tool for building subject collections. UNSILO Evaluate APIs are tools to aid the manuscript submission process. UNSILO works with world-leading content owners in science, legal and

corporate R&D to improve discoverability across their platforms and improving internal publishing processes.