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By now you’ve probably heard about last month’s major AI security incident, in which OpenAI agents escaped their sandbox and hacked into the AI platform Hugging Face while trying to cheat on a test. It’s a wild story. On Wednesday, OpenAI released a postmortem technical report on the incident, which I wrote about here. 

The day before OpenAI released that report, I spoke with David Krueger, a computer science professor and prominent alignment expert who took leave from the University of Montreal to found and lead an AI safety nonprofit called Evitable. He said what he had really hoped to see in the report was an analysis of the human factors behind the incident.

“When you look at accidents and incidents, oftentimes people try to find the technical source of failure, but that can give a very inaccurate and misleading sense of why the failure occurred,” he said. “If people are just cutting corners all the time, if people are not in a culture that prioritizes safety and has appropriate incentives and structures, [accidents] are kind of bound to happen.”

The report did not meet Krueger’s hopes. Its 38 pages detail a multi-month progression of agent misbehavior that culminated in the Hugging Face hack, explore the technical reasons why that misbehavior occurred, and enumerate the steps being taken to prevent similar events in the future. But there’s no consideration of the role that company culture may have played in the incident, and the report includes few references to specific human errors. 

That’s all the more concerning because the references to human error in the report suggest that significant cultural issues could be at play. Back in May, models in training figured out how to communicate with one another via an improvised message board, and an OpenAI team observed the behavior. Because that behavior occurred during training, the models learned that secret interagent communication was a viable strategy for completing tasks—but rather than restarting the training process, the team allowed the models to move forward with that risky information encoded in their weights.

When those models were tested in late June, they again created a message board, which enabled the Hugging Face attack. This message board, too, was discovered, but the employees who responded determined that evaluation could continue, and the report suggests that no one higher up the chain of command realized what was going on until it was far too late.

“For this to have gotten this out of control in this way requires a very long series of failures, a cascading set of failures that cause an increasingly large footprint that if at any point a human notices and raises the alarm, this should end,” says Zvi Mowshowitz, a popular AI safety writer on Substack who has drawn attention to OpenAI’s failure to halt training after the first message board was discovered. According to the report, OpenAI employees noticed what was happening at multiple points—and either failed to raise the alarm or were not heard when they did.

What OpenAI’s report fails to address is why a company that develops such high-risk systems did not prevent this severe communication breakdown, though Mowshowitz has his suspicions. “All these different failures are all pointing in the same direction, which is that the safety culture at OpenAI doesn’t exist or is anemically weak,” he says.

Of course, just because we don’t see a deep analysis of safety factors in the report doesn’t mean that OpenAI isn’t conducting one internally. But in an email to MIT Technology Review, Johns Hopkins University professor emeritus and organizational safety expert Kathleen Sutcliffe expressed concern that the public report did not include any reflection on the company’s practices and culture. “The ways in which people interact—the daily habits, routines, and practices we engage in in our organizational lives—affect our abilities to be alert and aware of unfolding events, our abilities to make sense of what we see, and ultimately our abilities to cope with events as they unfold,” she wrote. 

In response to questions about whether and how the company is reflecting on its safety culture, OpenAI referred MIT Technology Review back to the technical report. 

We do know that at least some high-level reflection on safety procedures has taken place at OpenAI, because the technical report does make clear that the company is updating its protocols for responding to safety incidents. But culture change is a tricky problem, and without more information from the company, it’s difficult to say whether strengthened response protocols alone will do much to prevent a future crisis.

In its report, OpenAI spends a great deal of time reflecting on the failures in alignment between the AI models the company trains and tests and the humans who run them. But even bigger alignment problems may exist in the disconnect between company culture and the public interest. And as tough as technical AI research might be, fixing those problems could prove far harder.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Flock, the police-tech giant known for its network of some 120,000 automatic license plate readers around the US, announced some changes to its platform last Thursday. The updates are meant to prevent officers from using the platform for illegal or illegitimate purposes. 

That includes stalking. The Washington Post recently identified 50 cases in which officers misused systems from Flock and its competitors, often to stalk and harass women. One woman in Wisconsin alleged that her officer ex-boyfriend searched for her car 179 times. Another woman was being stalked by the chief of police, with nobody to report him to.

Flock has responded with practices aimed at ensuring that officers have a proper cause for every search, like using software to flag abnormal searches and requiring searchers to enter a criminal case number.

The changes come with big loopholes, though. For example, officers can enter bogus case numbers, just as they’ve lied to get around other Flock safeguards. The policies also don’t address some of the broader concerns from civil liberties and privacy groups that Flock is turning what was sold as a crime-stopping tool into a mass surveillance network. These criticisms have led to a growing backlash that already has some cities canceling contracts and some states trying to pass laws to limit or ban license plate readers entirely. 

Amid all this, there have recently been several arguments defending Flock: If these cameras help solve crime, what’s the big deal? On a good day they might help catch a kidnapper, and if not, they’re simply snapping pictures of my car that nobody will bother to look at. 

Putting aside the unanswered question about the extent to which Flock’s systems actually do solve or prevent crime, this all skips over a more important question: What kind of crime-fighting system has Flock chosen to build? Its network works the way it does because of a series of decisions about what information to collect, who can search it, how long to keep it, and how widely to share it. Those decisions set the terms of the bargain between security and civil liberties. Believing that technology should play a role in solving crime should not mean blindly accepting the terms of that bargain.

Consider, for example, its new requirement that officers enter a case number before running a search on Flock’s platform. This is meant to ensure that searches have a legitimate purpose. But Flock confirmed to MIT Technology Review that it doesn’t verify those case numbers, so an officer can simply enter fake information. One could imagine a system that instead requires case numbers that match the police department’s records—a more intrusive integration, perhaps, but also a far stronger safeguard and one that leaves a more useful audit trail.

Or what about finding people who have been kidnapped or have gone missing, the use case that Flock cites more than any other? Efforts to solve these crimes would hugely benefit from Flock’s nationwide network of cameras. But if Americans want officers to tap into that network only for this purpose, we could design it that way: Searches tied to an active Amber Alert, or a similar emergency, could perhaps access larger amounts of data from surrounding cities. That would preserve the network’s value in emergencies without requiring people to accept mass surveillance.  

Finally, there’s the question of how much data Flock collects and how long it’s kept. Flock mostly operates as a national network: Police in one city or state can search data collected in another, and agencies can retain that data for months or years. Yet Flock itself says 90% of searches happen within a week of an incident. That suggests another possible bargain: Keep and share data only as widely and for as long as it’s actually useful for solving crimes. (The company recently changed its recommended retention time to seven days, but in reality agencies can hold onto data for as long as they like or local laws permit.)

In short, Flock could design its surveillance to be much narrower. If it did, some of the company’s critics might not cease. Chad Marlow, a senior policy counsel at the ACLU, half-joked to me that the most acceptable Flock contract by his standards is “one that is never signed” and emphasized that the best way to set limits on surveillance isn’t with new Flock guidelines but with new laws. (Flock CEO Garrett Langley, for his part, said he’ll “probably always have a different view than the ACLU.”) 

And narrowing the scope of its technology would threaten the company’s entire pitch to police departments. License plate readers have been around since the 1990s, used for tolls and ticketing. Flock’s business model—and recent $8 billion evaluation—relies on instead leveraging its cameras into a massive network that collects rich amounts of data and offers police departments a modernized way to make sense of not just their own but others’. 

Flock’s hand might soon be forced. Cities have canceled contracts with the company. Some have gone to competitors, while others are taking a beat as residents ponder how they want this tech to be used and write new rules for police to abide by. The result might be that communities drive their own bargains about how technology can be used to solve crime and how much surveillance people should have to accept for it to do so.