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AI AI · 2 MIN READ

Fired OpenAI safety researchers deny misconduct and warn of chilling effect

Three OpenAI safety researchers—Jasmine Wang, Tomek Korbak, and Mikita Balesni—published an open letter disputing the company’s claims that they mishandled sensitive information.

Three OpenAI safety researchers—Jasmine Wang, Tomek Korbak, and Mikita Balesni—published an open letter disputing the company’s claims that they mishandled sensitive information. The trio was fired last week after OpenAI accused them of violating policies by sharing confidential data with a third-party AI safety organization, according to techcrunch.com.

In their letter addressed to OpenAI’s Safety and Security Committee, Safety Advisory Group, and Mission Advisory Council, the researchers emphasized their concern that the firings have created a chilling effect within the company. They argued that their dismissal undermines the culture of open collaboration and internal procedures that previously allowed safety teams to work closely with external experts without fear of retaliation.

The researchers highlighted that AI development involves unique risks that safety teams identify earlier than others, making collaboration with outside experts essential. They warned that restricting such communication could weaken safety mechanisms. OpenAI’s statement had accused them of mishandling sensitive information, but the researchers maintain that their actions were part of established safety practices, signaling a shift in the company’s approach to transparency and safety culture.

The open letter was published on Thursday, shortly after the firings last week, drawing attention to internal tensions at OpenAI regarding safety research protocols and external collaboration. The letter explicitly calls on OpenAI’s advisory bodies to reconsider the impact of these firings on the company’s safety mission and culture.

Editorial standards. Reported and edited at Startupniti's news desk from the sources listed in the right rail. Every fact traces to a citation. If something looks wrong, write to corrections.
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