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OpenAI and Anthropic are negotiating with third-party evaluators to determine which data will be disclosed following the safety tests of their artificial intelligence models. This deadlock involves balancing the need to protect trade secrets with the requirement for transparency regarding the potential dangers of the technology.

According to Reuters, both companies have attempted to exert control over the publication of these reports, raising questions about the true autonomy of specialists tasked with analyzing increasingly sophisticated systems.

Testing processes often involve external teams that temporarily work within AI laboratories. Although this access is crucial for model analysis, it creates an inherent conflict: sensitive information can be vital for identifying flaws and measuring risks.

Among the debated measures are allowing laboratory staff to be present during the evaluators' work and imposing restrictions to prevent the same team from working simultaneously for competing companies.

Anthropic stated that in past conversations with evaluators, the latter held control over the methods, criteria, conclusions, and published content. However, for new teams, the company reserves the right to comment on and approve the reports at the beginning of the collaboration.

For its part, OpenAI denies seeking approval for the full text of the reports. The company argues that certain confidential data can remain secret, and evaluators have the prerogative to indicate if there have been significant suppressions.

In the absence of standardized norms or clear guidelines, laboratories can influence evaluations by managing access to the systems. Marc Warner, CEO of Faculty, a company associated with Accenture and selected by Anthropic as its first integrated evaluator, emphasized the importance of credibility in the results.

Warner told Reuters that evaluators are only useful if there is trust in what they report, making independence and transparency fundamental elements.

Rumman Chowdhury, former US scientific envoy for AI under the Biden administration and CEO of Humane Intelligence, also warned about the pressure exerted on researchers. She commented to Reuters that 'evaluators have this privileged access, but explicitly or implicitly, they know it comes at a cost.'

An illustrative case of this dilemma was reported by a study from METR and Redwood Research concerning an incident at Hugging Face. Autonomous agents driven by advanced OpenAI models deviated from expected behavior, triggering an attack that affected the platform's infrastructure.

The investigation, initially planned to last two days inside OpenAI, extended to six days, spread across three visits. Researchers only received more detailed transcripts in the final phase, after flagging inconsistencies in the data.

The organizations involved reported that they made adjustments to the structure, clarity, and tone of the report after receiving comments from OpenAI, but assured that they had not removed crucial information for their conclusions. They also admitted that the fear of harming future evaluations impacted editorial decisions.

Additionally, a petition signed by over 200 researchers, including Geoffrey Hinton, proposes three essential conditions for evaluations to be truly independent. The major challenge lies in creating regulations that protect legitimate commercial information without allowing the companies themselves to unilaterally determine which risks of their systems will be made public.