Our members place their trust in us, and expect and deserve a safe and trusted community where they can express themselves professionally. We are constantly investing in a variety of systems (Automated Fake Account Detection at LinkedIn, Defending Against Abuse at LinkedIn’s Scale, and How We’re Protecting Members From Fake Profiles) to help automatically detect...
trust engineering Articles
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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:...
- Topics:
- machine learning,
- Open Source,
- trust engineering
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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...
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At LinkedIn, we are entrusted with protecting the privacy and data of our members. Storing members’ choices on how they intend their...
- Topics:
- infrastructure,
- scale,
- trust engineering
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As members explore the many products of LinkedIn, including the feed, homepage, Learning, Recruiter, and Sales Navigator, to name a...
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Co-authors: Krishnaram Kenthapadi, Thanh Tran, Mark Dietz, and Ian Koeppe Preserving privacy of users is a key requirement of...
- Topics:
- trust engineering,
- Analytics,
- research,
- Data