{"id":45454,"date":"2023-05-31T13:07:36","date_gmt":"2023-05-31T13:07:36","guid":{"rendered":"https:\/\/carleton.ca\/fass\/?post_type=cu_story&p=45454"},"modified":"2025-01-28T16:15:53","modified_gmt":"2025-01-28T21:15:53","slug":"connecting-the-digital-dots","status":"publish","type":"cu_story","link":"https:\/\/carleton.ca\/fass\/story\/connecting-the-digital-dots\/","title":{"rendered":"Connecting the Digital Dots"},"content":{"rendered":"\n
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\n Connecting the Digital Dots\n <\/h1>\n \n <\/header>\n <\/div>\n\n <\/div>\n\n \n\n <\/div>\n<\/section>\n\n\n\n

History professor creates machine-learning method to help researchers dig deeper into antiquities trafficking<\/h2>\n\n\n\n

By Alyssa Tremblay<\/em><\/p>\n\n\n\n

How do you see something that you never thought to look for in the first place?<\/p>\n\n\n\n

This is the problem faced by those who study antiquities trafficking \u2014 the thieving, looting and illicit trading of cultural artefacts \u2014 and the murky international networks that sustain it.<\/p>\n\n\n\n

To overcome this challenge, Carleton digital archaeologist Shawn Graham<\/a> 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.<\/p>\n\n\n\n

Published in the latest issue of Advances in Archaeological Practice<\/a><\/em>, 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\u2019s most notorious figures.<\/p>\n\n\n

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\u201cIn the digital humanities, there\u2019s an idea called \u2018deformation\u2019: 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.\u201d<\/p>\nProfessor Shawn Graham, Department of History<\/cite><\/blockquote>\n<\/div>\n\n\n

The approach in question is a machine-learning model that uses established facts about the illegal antiquities trade to identify previously unnoticed possible connections.<\/p>\n\n\n\n

Graham created the model with help from Carleton data scientist Ahmed El-Roby<\/a>, making the project one of the first-ever collaborations between the university\u2019s School of Computer Science and the Department of History.<\/p>\n\n\n\n

He also worked alongside archaeologist Donna Yates from Maastricht University, whose involvement in the Trafficking Culture Encyclopedia \u2014 an online repository of over 100 researcher-submitted case studies describing documented instances of trafficking between individuals, museums and art galleries \u2014 helped provide the model with a \u201cperfect test set\u201d of data to work from.<\/p>\n\n\n\n

According to their co-authored paper, the machine-learning model used the information in these encyclopedia entries to suggest \u201cconnections between actors and institutions hitherto unsuspected and not otherwise present\u201d in the existing data, producing leads or \u201cgood tips\u201d for researchers to follow up on.<\/p>\n\n\n\n

Think of it less as the hardworking detective pounding the streets for clues and solving the case, and more as the perceptive sidekick who notices an overlooked detail when examining all the gathered evidence.<\/p>\n\n\n\n

\u201cThe model doesn\u2019t tell us fact; it gives us suggestions that we would likely have never considered on our own,\u201d explains Yates. \u201cWe don\u2019t know if a tip is true or not \u2014 we have to do investigative work to determine that. But since it is based on information that is<\/em> true, we can consider it worth following up on.\u201d<\/p>\n\n\n\n

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Professor Shawn Graham, Department of History<\/figcaption><\/figure>\n\n\n\n

Indeed, following up on the very first tip produced by this method has already led to intriguing evidence about how a well-known convicted Latin American antiquities criminal seems to have laundered his reputation by making small donations to museums in advance of major sales, tax evasion or fraud schemes.<\/p>\n\n\n\n

\u201cThis is an aspect of how he operated that we\u2019ve never documented before,\u201d Yates says. \u201cIt shows a pattern within his crime that we would have never thought to investigate, if the model hadn’t suggested it.\u201d<\/p>\n\n\n\n

Yates presented the group\u2019s research findings to archaeologists, prosecutors, police and financial crimes unit investigators at a recent training event in Mexico City hosted by the Mexican government and the EU\u2019s Global Facility on Anti-Money Laundering and Counter Terrorism Financing.<\/p>\n\n\n\n

As for the future, Graham is working on using artificial intelligence to streamline the methodology even further, semi-automating the process and drastically reducing the amount of time previously spent annotating the information that gets fed into the model.<\/p>\n\n\n\n

\u201cWhat took us several months of annotation by hand can be done in an afternoon now with the AI,\u201d he says, emphasizing how that hard and necessary work was made possible thanks to the efforts of Carleton History graduate students Chantal Brousseau, Jonah Ellens and Callum McDermott.<\/p>\n\n\n\n

\u201cOur grad student co-authors were so important to this entire process, from gathering and annotating data, to figuring out the data transformations and coding, to error checking and writing. We couldn\u2019t have got this far without Chantal, Jonah and Callum.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"

History professor creates machine-learning method to help researchers dig deeper into antiquities trafficking By Alyssa Tremblay 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 \u2014 the thieving, looting and illicit trading of cultural artefacts \u2014 and […]<\/p>\n","protected":false},"author":21,"featured_media":45450,"template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"cu_story_type":[575],"cu_story_tag":[],"class_list":["post-45454","cu_story","type-cu_story","status-publish","has-post-thumbnail","hentry","cu_story_type-research"],"acf":{"cu_post_thumbnail":""},"_links":{"self":[{"href":"https:\/\/carleton.ca\/fass\/wp-json\/wp\/v2\/cu_story\/45454","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/carleton.ca\/fass\/wp-json\/wp\/v2\/cu_story"}],"about":[{"href":"https:\/\/carleton.ca\/fass\/wp-json\/wp\/v2\/types\/cu_story"}],"author":[{"embeddable":true,"href":"https:\/\/carleton.ca\/fass\/wp-json\/wp\/v2\/users\/21"}],"version-history":[{"count":5,"href":"https:\/\/carleton.ca\/fass\/wp-json\/wp\/v2\/cu_story\/45454\/revisions"}],"predecessor-version":[{"id":51499,"href":"https:\/\/carleton.ca\/fass\/wp-json\/wp\/v2\/cu_story\/45454\/revisions\/51499"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/carleton.ca\/fass\/wp-json\/wp\/v2\/media\/45450"}],"wp:attachment":[{"href":"https:\/\/carleton.ca\/fass\/wp-json\/wp\/v2\/media?parent=45454"}],"wp:term":[{"taxonomy":"cu_story_type","embeddable":true,"href":"https:\/\/carleton.ca\/fass\/wp-json\/wp\/v2\/cu_story_type?post=45454"},{"taxonomy":"cu_story_tag","embeddable":true,"href":"https:\/\/carleton.ca\/fass\/wp-json\/wp\/v2\/cu_story_tag?post=45454"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}