TY - GEN
T1 - AI-SNIPS
T2 - 37th AAAI Conference on Artificial Intelligence, AAAI 2023
AU - Burt, Timothy A.
AU - Passas, Nikos
AU - Kakadiaris, Ioannis A.
N1 - Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2023/6/27
Y1 - 2023/6/27
N2 - This paper presents AI-SNIPS (AI Support for Network Intelligence-based Pharmaceutical Security), a production-ready platform that enables stakeholder decision-making, secure data sharing, and interdisciplinary research in the fight against Illicit, Substandard, and Falsified Medical Products (ISFMP). AI-SNIPS takes as input cases: a case consists of one or more URLs suspected of ISFMP activity. Cases can be supplemented with ground-truth structured data (labeled keywords) such as seller PII or case notes. First, AI-SNIPS scrapes and stores relevant images and text from the provided URLs without any user intervention. Salient features for predicting case similarity are extracted from the aggregated data using a combination of rule-based and machine-learning techniques and used to construct a seller network, with the nodes representing cases (sellers) and the edges representing the similarity between two sellers. Network analysis and community detection techniques are applied to extract seller clusters ranked by profitability and their potential to harm society. Lastly, AI-SNIPS provides interpretability by distilling common word/image similarities for each cluster into signature vectors. We validate the importance of AI-SNIPS's features for distinguishing large pharmaceutical affiliate networks from small ISFMP operations using an actual ISFMP lead sheet.
AB - This paper presents AI-SNIPS (AI Support for Network Intelligence-based Pharmaceutical Security), a production-ready platform that enables stakeholder decision-making, secure data sharing, and interdisciplinary research in the fight against Illicit, Substandard, and Falsified Medical Products (ISFMP). AI-SNIPS takes as input cases: a case consists of one or more URLs suspected of ISFMP activity. Cases can be supplemented with ground-truth structured data (labeled keywords) such as seller PII or case notes. First, AI-SNIPS scrapes and stores relevant images and text from the provided URLs without any user intervention. Salient features for predicting case similarity are extracted from the aggregated data using a combination of rule-based and machine-learning techniques and used to construct a seller network, with the nodes representing cases (sellers) and the edges representing the similarity between two sellers. Network analysis and community detection techniques are applied to extract seller clusters ranked by profitability and their potential to harm society. Lastly, AI-SNIPS provides interpretability by distilling common word/image similarities for each cluster into signature vectors. We validate the importance of AI-SNIPS's features for distinguishing large pharmaceutical affiliate networks from small ISFMP operations using an actual ISFMP lead sheet.
UR - http://www.scopus.com/inward/record.url?scp=85168246140&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85168246140&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85168246140
T3 - Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
SP - 16407
EP - 16409
BT - AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations
A2 - Williams, Brian
A2 - Chen, Yiling
A2 - Neville, Jennifer
PB - AAAI Press
Y2 - 7 February 2023 through 14 February 2023
ER -