Co-authors: Guillaume Saint-Jacques, Amir Sepehri, Nicole Li, and Igor Perisic Introduction Previously on this blog, we’ve shared information on best practices in data science, particularly in areas such as A/B testing. We’ve also discussed the importance of ethics in fields such as data science, early implementations of “fairness by design” principles in our...
machine learning Articles
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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...
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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...
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Co-authors: Ali Mohamed and Zheng Li LinkedIn’s feed stands at the center of building global professional knowledge-sharing...
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“Make LinkedIn feel instantaneous” is a mantra our performance team lives by to deliver the best possible experience to our members....
- Topics:
- Performance,
- experimentation,
- machine learning