Who decides what AI tells you? Ex-Meta news chief Campbell Brown weighs in.

Throughout her career, Campbell Brown has pursued accurate information—first as a celebrated television journalist, then as Facebook’s first and only dedicated news chief. Now, as AI reshapes how people consume information, she sees history threatening to repeat itself. This time, she isn’t waiting for someone else to fix it.

Her company, Forum AI—which she recently discussed with TechCrunch’s Tim Fernholz at a StrictlyVC evening in San Francisco—evaluates how foundation models perform on what she terms “high-stakes topics”: geopolitics, mental health, finance, and hiring. These are subjects where “there are no clear yes-or-no answers, where it’s murky, nuanced, and complex.”

The approach involves finding the world’s foremost experts, having them design benchmarks, and then training AI judges to evaluate models at scale. For Forum AI’s geopolitics work, Brown has recruited Niall Ferguson, Fareed Zakaria, former Secretary of State Tony Blinken, former House Speaker Kevin McCarthy, and Anne Neuberger, who led cybersecurity under the Obama administration. The goal is to achieve roughly 90% consensus between AI judges and those human experts—a threshold she says Forum AI has already reached.

Brown traces the origin of Forum AI—founded 17 months ago in New York—to a specific moment. “I was at Meta when ChatGPT was first released publicly,” she recalled, “and I remember realizing shortly after that this is going to be the funnel through which all information flows. And it’s not very good.” The implications for her own children made the moment feel almost existential. “My kids are going to be really dumb if we don’t figure out how to fix this,” she remembers thinking.

What frustrated her most was that accuracy didn’t seem to be anyone’s priority. Foundation model companies, she said, are “extremely focused on coding and math,” while news and information are more difficult. But harder, she argued, doesn’t mean optional.

Indeed, when Forum AI began evaluating the leading models, the findings weren’t exactly encouraging. She cited Gemini pulling from Chinese Communist Party websites “for stories that have nothing to do with China,” and noted a left-leaning political bias across nearly all models. Subtler failures also abound, she said, including missing context, missing perspectives, and straw-manning arguments without acknowledgment. “There’s a long way to go,” she said. “But I also think there are some very easy fixes that would vastly improve the outcomes.”

Brown spent years at Facebook watching what happens when a platform optimizes for the wrong metrics. “We failed at a lot of the things we tried,” she told Fernholz. The fact-checking program she built no longer exists. The lesson—even if social media has turned a blind eye to it—is that optimizing for engagement has been detrimental to society and left many people less informed.

Her hope is that AI can break that cycle. “Right now it could go either way,” she said; companies could give users what they want, or they could “give people what’s real and what’s honest and what’s truthful.” She acknowledged that the idealistic version—AI optimizing for truth—might sound naive. But she thinks enterprise may be an unlikely ally here. Businesses using AI for credit decisions, lending, insurance, and hiring care about liability, and “they’re going to want you to optimize for getting it right.”

That enterprise demand is also what Forum AI is betting its business on, though turning compliance interest into consistent revenue remains a challenge, particularly given that much of the current market is still satisfied with checkbox audits and standardized benchmarks that Brown considers inadequate.

The compliance landscape, she said, is “a joke.” When New York City passed the first hiring bias law requiring AI audits, the state comptroller found that more than half had violations that went undetected. Real evaluation, she said, requires domain expertise to work through not just known scenarios but edge cases that “can get you into trouble that people don’t think about.” And that work takes time. “Smart generalists aren’t going to cut it.”

Brown—whose company last fall raised $3 million led by Lerer Hippeau—is uniquely positioned to describe the disconnect between the AI industry’s self-image and the reality for most users. “You hear from the leaders of the big tech companies, ‘This technology is going to change the world,’ ‘it’s going to put you out of work,’ ‘it’s going to cure cancer,’” she said. “But then to a normal person who’s just using a chatbot to ask basic questions, they’re still getting a lot of slop and wrong answers.”

Trust in AI sits at extraordinarily low levels, and she thinks that skepticism is, in many cases, justified. “The conversation is sort of happening in Silicon Valley around one thing, and a totally different conversation is happening among consumers.”