LinkedIn AI Training Data Lawsuit Dismissed

A lawsuit accusing LinkedIn of using user messages to train AI models has been dismissed.

Plaintiff Alessandro De La Torre, who filed the proposed class action suit last week, has since withdrawn his complaint, Reuters reported Friday (Jan. 31).

The suit accused the Microsoft-owned LinkedIn of disclosing its premium customers’ private messages to third parties without their consent to train generative artificial intelligence (AI) models. LinkedIn had said the case had no merit.

“LinkedIn’s belated disclosures here left consumers rightly concerned and confused about what was being used to train AI,” Eli Wade-Scott, managing partner at Edelson PC, which represented De La Torre, told Reuters.

“Users can take comfort, at least, that LinkedIn has shown us evidence that it did not use their private messages to do that,” he added. “We appreciate the professionalism of LinkedIn’s team.”

On Thursday (Jan. 30), LinkedIn vice president of legal Sarah Wight posted this message: “Sharing the good news that a baseless lawsuit against LinkedIn was withdrawn earlier today. It falsely alleged that LinkedIn shared private member messages with third parties for AI training purposes. We never did that. It is important to always set the record straight.”

LinkedIn’s privacy policy update and use of users as unwitting trainers of its AI models kicked off a firestorm over data privacy and consumer trust, PYMNTS reported in September.

The company’s move could cause businesses to reconsider their digital footprint because of the risk of compromising sensitive information, David McInerney, commercial manager for data privacy at Cassie, said in an interview here at the time.

“A whopping 93% [of consumers] are concerned about the security of their personal information online,” McInerney said.

Meanwhile, PYMNTS wrote last month about warnings from AI experts that training systems for artificial intelligence models could be hitting their limit.

“Internet data is running out, and AI companies are feeling the pressure,” Arunkumar Thirunagalingam, senior manager of data and technical operations at the McKesson Corporation, told PYMNTS.

“For years, they relied on scraping huge amounts of online content to train their systems. That worked for a while, but now the easy data is drying up. This shift is putting the spotlight on companies with unique data sources, like healthcare records or logistics information. It is no longer about how much data you can grab; it is about having the right kind of data.”