Peer Review of Scholarly Research Gets an AI Boost
In the world of academics, peer review is considered the only credible validation of scholarly work. Although the process has its detractors, evaluation of academic research by a cohort of contemporaries has endured for over 350 years, with relatively minor changes." However, peer review may be set to undergo its biggest revolution ever-the integration of artificial intelligence.
Open-access publisher Frontiers has debuted an AI tool called the Artificial Intelligence Review Assistant (AIRA), which purports to eliminate much of the grunt work associated with peer review. Since the beginning of June 2020, every one of the 11,000-plus submissions Frontiers received has been run through AIRA, which is integrated into its collaborative peer-review platform. This also makes it accessible to external users, accounting for some 100,000 editors, authors, and reviewers. Altogether, this helps maximize the efficiency of the publishing process and make peer-review more objective," says Kamila Markram, founder and CEO of Frontiers.
AIRA's interactive online platform, which is a first of its kind in the industry, has been in development for three years.. It performs three broad functions, explains Daniel Petrariu, director of project management: assessing the quality of the manuscript, assessing quality of peer review, and recommending editors and reviewers. At the initial validation stage, the AI can make up to 20 recommendations and flag potential issues, including language quality, plagiarism, integrity of images, conflicts of interest, and so on. This happens almost instantly and with [high] accuracy, far beyond the rate at which a human could be expected to complete a similar task," Markram says.
We have used a wide variety of machine-learning models for a diverse set of applications, including computer vision, natural language processing, and recommender systems," says Markram. This includes simple bag-of-words models, as well as more sophisticated deep-learning ones. AIRA also leverages a large knowledge base of publications and authors.
Markram notes that, to address issues of possible AI bias, We...[build] our own datasets and [design] our own algorithms. We make sure no statistical biases appear in the sampling of training and testing data. For example, when building a model to assess language quality, scientific fields are equally represented so the model isn't biased toward any specific topic." Machine- and deep-learning approaches, along with feedback from domain experts, including errors, are captured and used as additional training data. By regularly re-training, we make sure our models improve in terms of accuracy and stay up-to-date."
The AI's job is to flag concerns; humans take the final decisions, says Petrariu. As an example, he cites image manipulation detection-something AI is super-efficient at but is nearly impossible for a human to perform with the same accuracy. About 10 percent of our flagged images have some sort of problem," he adds. [In academic publishing] nobody has done this kind of comprehensive check [using AI] before," says Petrariu. AIRA, he adds, facilitates Frontiers' mission to make science open and knowledge accessible to all.