Stricter verification on LinkedIn cut job-recommendation precision by 12% for users needing extra vetting, a new study finds, highlighting a trade-off between trust and AI matching.
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New research shows LinkedIn's stricter verification and content-moderation rules reduced the precision of its job-recommendation algorithms by roughly 12% for users whose profiles required additional vetting. The working paper, posted to arXiv, examined the trade-off between information assurance and algorithmic performance on the platform.
The study, titled "The Efficiency Costs of Information Assurance in AI-Enabled Labor Markets: Evidence from LinkedIn's Policy Changes," analyzed how the policy shifts affected the platform's AI-driven matching system. LinkedIn introduced the changes to combat misinformation and fake profiles, a move aimed at improving trust in the platform's data. The added friction in profile validation created a trade-off: more accurate user information came at the cost of less effective AI matching for certain segments of the workforce.
The effect was most pronounced for workers in non-traditional roles or those with gaps in their employment history, the paper said. The algorithms, trained on cleaner but smaller datasets after the policy changes, struggled to surface relevant opportunities for these users compared with the pre-policy baseline.
LinkedIn did not immediately respond to a request for comment on the findings. The paper's authors said the results highlight a tension between platform governance and algorithmic performance that platforms like LinkedIn, X, and Meta face as they tighten content rules.
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