The following short excerpt is from an article by Alyssa Tremblay describing the new machine-learning methodology that History Professor Shawn Graham and his team have designed to examine the complex networks involved in antiquities trafficking. The full article, “Connecting the Digital Dots: History professor creates machine-learning method to help researchers dig deeper into antiquities trafficking,” is available online.
How do you see something that you never thought to look for in the first place?
This is the problem faced by those who study antiquities trafficking — the thieving, looting and illicit trading of cultural artefacts — and the murky international networks that sustain it.
To overcome this challenge, Carleton digital archaeologist Shawn Graham and his team have designed a groundbreaking methodology that lets researchers re-examine this complex web of illegal activities with a fresh pair of AI eyes.
Published in the latest issue of Advances in Archaeological Practice, a journal of the Society for American Archaeology, Graham’s methodology has caught the attention of academics and law enforcement alike, resulting in promising new research into some of the trafficking world’s most notorious figures.
“In the digital humanities, there’s an idea called ‘deformation’: What might we see if we could look at something familiar as if it were brand new, entirely alien and utterly fresh? I think of this approach in those terms, in that it deforms what we know such that new possibilities are made visible.”
Professor Shawn Graham, Department of History
The approach in question is a machine-learning model that uses established facts about the illegal antiquities trade to identify previously unnoticed possible connections.
Graham created the model with help from Carleton data scientist Ahmed El-Roby, making the project one of the first-ever collaborations between the university’s School of Computer Science and the Department of History.
He also worked alongside archaeologist Donna Yates from Maastricht University, whose involvement in the Trafficking Culture Encyclopedia — an online repository of over 100 researcher-submitted case studies describing documented instances of trafficking between individuals, museums and art galleries — helped provide the model with a “perfect test set” of data to work from.