The Anti-Abuse AI Team at LinkedIn creates, deploys, and maintains models that detect and prevent various types of abuse, including the creation of fake accounts, member profile scraping, automated spam, and account takeovers. There are several unique challenges we face when using machine learning to prevent abuse on a large professional network, including:...

Co-authors: Yu Li, Szymon Gizecki, and Chinmaya Dattathri To ensure we have significant flexibility in how our teams collaborate, our trunk-based engineering development workflow manages dependencies on a binary level, instead of source level. This requires very efficient and sophisticated management of the resulting dependency graph, and we discussed our...

Co-authors: Stuart Ambler, Ahsan Chudhary, Mark Dietz, Sahin Cem Geyik, Krishnaram Kenthapadi, Ian Koeppe, Varun Mithal, Guillaume Saint-Jacques, Amir Sepehri, Thanh Tran, and Sriram Vasudevan Editor’s note: A shorter version of this article was originally posted by Krishnaram Kenthapadi on LinkedIn. Introduction How do we take fairness and transparency into...