I've been a bit quiet on the blog lately, due to travelling, and attending the North American Regional Science Conference in San Diego. Regional science is a multidisciplinary field that overlaps economics, geography, planning, and many other disciplines. There were many interesting sessions at the conference, although apparently not the session that I was presenting in, which had an audience of two (one of which was another speaker in the session, with the other three speakers in my session no-showing).
Anyway, one of the most interesting sessions was the day before the conference proper started, on "The Potential of AI for Regional Science", hosted by The Regional Science Academy. Speakers included many of the contemporary great minds in regional science such as Rick Church (University of California, Santa Barbara), Tomaz Dentinho (University of Azores), Peter Nijkamp (Open University), and John Östh (Oslo Metropolitan University), among others. They noted (with examples) the great opportunities that artificial intelligence offers, especially as an aid for research. For example, Östh demonstrated a really interesting application of AI to generating data on neighbourhoods, while Patricio Aroca (Universidad Andres Bello) showed how AI could help researchers whose first language is not English to improve their chances of publication in top-ranked English language journals.
I came away from this session in equal parts excited and concerned (which might be an appropriate mix of reactions to any sufficiently disruptive technology). There is great potential in using AI as a research tool, and I've already seen many examples (and blogged about some ideas here and here). However, my concern comes from how AI will change the research production function.
Before we get that far, let's go back to an earlier technological revolution that greatly changed how research in economics was conducted. This story is not mine, but I forget where I got it from. Consider a simple research production function in economics, with two inputs: (1) econometric modelling; and (2) economic explanations. As computers increased in computational power, the relative price of econometric modelling decreased. So, researchers reallocated their scarce research resources from economic explanations (which had become relatively more expensive as an input) towards econometric modelling (which had become relatively less expensive as an input). The result was a rapid increase in econometric modelling, and a corresponding decline in the quality of economic explanations to go along with the econometric modelling. It is not clear that this was a positive change for the overall quality of economics research (but there certainly was an increase in the quantity of research).
Now consider a similar research production function in economics, where the inputs are: (1) human intuition and explanations; and (2) artificial intelligence. New AI models have made AI radically less expensive to use. We can probably expect research in economics (and in other fields) to adjust to much more use of AI, and much less use of human intuition and explanations. It remains to be seen whether that leads to an increase or a decrease in the quality of research overall. My money is on an overall increase in the quantity of research, but a reduction in the average quality and an increase in the variance (with some very high quality research resulting from AI, as well as a lot of very low quality research).
Unlike the science fiction and fantasy magazine Clarkesworld, which shut off submissions earlier this year due to a flood of AI-generated stories were submitted, I'm not aware of any academic publishers that are feeling the strain, yet. It will be coming though, and may intersect with the increasing volume of predatory publishers (see here and here). As the Managing Editor of a journal myself (the Australasian Journal of Regional Studies), I'm really not looking forward to policing the submission of AI-generated articles of low quality.
Taken altogether, it is clear that there is a trade-off inherent in the impact of AI on research (in regional science, in economics, and in other fields). We will need to accept (and act to mitigate) the negative impacts, while endeavouring to maximise the good.
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