Artificial Intelligence and Peer Review
Despite the fact that, for many people, it still feels like the middle of March, we have somehow made it to September and find ourselves celebrating the sixth annual Peer Review Week! This year’s theme is Trust, and what better way to celebrate than to look back on some of the amazing developments and discussions happening around peer review and natural language processing (NLP).
In April’s episode of RoRICast, the podcast produced by the Research on Research Institute that Digital Science co-founded a year ago, my co-host Adam Dinsmore and I chatted to Professor Karim Lakhani, the Charles E. Wilson Professor of Business Administration and the Dorothy and Michael Hintze Fellow at Harvard Business School. Karim is an expert in the application of artificial intelligence in research processes, from collaboration to peer review.
Karim joined us from his home in marvellous Massachusetts. Although an MIT graduate, Karim is now based across the river at Harvard Business School. His research involves analysing a range of open source systems to better understand how innovation in technology works. One of his specific research interests is in contest-driven open innovation and how, by throwing problems open to the wider world, we are often able to engage with a range of as yet unexplored solutions, owing to the different approaches a fresh perspective can bring.
Having determined that science is both a collaborative and competitive process, Karim and his team run experiments to better understand how teams are formed, and how different novel ideas are evaluated. Karim is also investigating the impact of artificial intelligence (AI) on organisations in terms of optimising scale and scope and gathering insights to help shape future business strategy.
Mirroring the experiences of Digital Science’s own Catalyst Grant judges and mentors, Karim has seen a rise in machine-learning based tech solutions at innovation contests. His latest book, Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World, includes examples of how AI is now not only having an impact on technology and innovation but also on our everyday lives. Karim’s work informs best practice in research and innovation by conducting research on research.
In this episode of RoRICast, Karim gave us some examples of how AI is not just confined to sci-fi movies and Neal Stephenson novels, though such stories give a great many examples of what is termed ‘strong AI’, capable of carrying out many tasks extremely efficiently. However, ‘weak AI’, that is tech that has been created to do one narrow task very well, has already permeated our everyday lives, whether that is through some of the NLP solutions we have discussed in this blog series, or whether it is something as commonplace as our voice-activated smart devices capable of playing Disney songs on demand, our email spam filters, or even our Netflix recommendations.
Karim discussed some of the potential applications of AI in research, from facilitating collaboration between researchers to writing papers. He also discussed how research can implement aspects of NLP within the research process that relate to peer review. For example, by using an NLP-driven tool such as Ripeta, researchers can receive recommendations on how to improve a paper prior to submission. Ripeta analyses the reproducibility and falsifiability of research, including everything from a well-reported methodology to the inclusion of data that adheres to FAIR principles.
With the rise of the open research movement, preprints have been gaining momentum as an important research output alongside the more traditional journal publications. This is particularly relevant in these current COVID-19 times, where research output is being produced at an unprecedentedly high volume, and many researchers are opting to share their work via preprint that undergoes an ongoing and dynamic review process rather than the more formal journal peer-review process.
A rise in preprint publication has been seen across almost all fields of research in 2020, in part due to the fact that many research areas contribute to solving the challenge of a global pandemic. This has however led to some concern over preprints, and whether they are a trustworthy research output without more formal peer review practices. It is here that a tool like Ripeta could add some level of trust, transparency, robustness and reliability to research shared via preprint even before the work is shared. The Ripeta team investigated this perceived lack of confidence in COVID-19 related preprints and found that although reporting habits in pandemic-related preprint publications demonstrated some room for improvement, overall the research being conducted and shared was sound.
The use of AI in peer review is a hot topic. There are many reasons to use AI in peer review, such as eliminating the potential conflict of interest posed by a reviewer in a very closely related field, or as a means to quickly assess the vast volume of submissions, again for example during a global pandemic. However, there are limitations to the technology, and factors that must be considered when determining whether the AI could be propagating and amplifying any areas of bias within the process, simply by failing to consider the bias within the training data fed to the programme, or by failing to eliminate said bias. As Joris van Rossum explained in his article on the limitations of tech in peer review, AI that has learned from historic decisions made is potentially able to reinforce imbalances and propagate the impact of unconscious biases in research.
Karim went on to describe the way that AI can be built to mitigate such circumstances that would actually lead to breaking down many barriers to inclusion by using AI, providing we as a community invest the time and effort into creating good data, testing the technology and ensuring that programs work fairly and ethically; an aspect of social science research that RoRI is particularly interested in. Furthermore, complementary AI could be used in other parts of the research process to eliminate many stumbling blocks that could be presented by reviewers on submitting a paper.
Using AI in peer review is just one example of open innovation to improve an aspect of research, but when can we expect to see this and other AI solutions being widely adopted as part of the research process? There is already a lot of tech around us, but within the next few years, this field will expand further as we learn more about how research works. By conducting research on research, researchers like Karim can uncover trends and connections in a range of research processes, and work towards creating tech solutions that will alleviate the burden and increase the efficiency of research.
We would like to thank Professor Karim Lakhani for giving up his time to join us for this episode of RoRICast. You can hear this whole episode of RoRICast here, and you can suggest future RoRICast topics and interviewees here.
We’ll be staying on the topic of peer review and pandemics by kicking off a mini-blog series tomorrow on the PREreview Project. Earlier this year a collaboration of publishers, industry experts and a preprint site (PREreview) joined together to respond to overwhelming levels of COVID-19 papers. Using information and feedback from the parties and reviewers involved, our authors Jon Treadway and Sarah Greaves examine what happened, whether the initiative succeeded, and what the results can tell us about peer review.