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Campbell Brown has spent her career chasing accurate information, first as a renowned TV journalist, then as Facebook\u2019s first, and only, dedicated news chief. Now, watching AI reshape how people consume information, she sees history threatening to repeat itself. This time, she\u2019s not waiting for someone else to fix it.<\/p>\n
Her company, Forum AI<\/a> \u2014 which she discussed recently with TechCrunch\u2019s Tim Fernholz at a StrictlyVC evening in San Francisco \u2014 evaluates how foundation models perform on what she calls \u201chigh-stakes topics\u201d \u2014 geopolitics, mental health, finance, hiring \u2014 subjects where \u201cthere are no clear yes-or-no answers, where it\u2019s murky and nuanced and complex.\u201d <\/p>\n The idea is to find the world\u2019s foremost experts, have them architect benchmarks, then train AI judges to evaluate models at scale. For Forum AI\u2019s 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 in the Obama administration. The goal is to get AI judges to roughly 90% consensus with those human experts, a threshold she says Forum AI has been able to reach.<\/p>\n Brown traces the origin of Forum AI, founded 17 months ago in New York, to specific moment. \u201cI was at Meta when ChatGPT was first released publicly,\u201d she recalled, \u201cand I remember really shortly after realizing this is going to be the funnel through which all information flows. And it\u2019s not very good.\u201d The implications for her own children made the moment feel almost existential. \u201cMy kids are going to be really dumb if we don\u2019t figure out how to fix this,\u201d she recalled thinking.<\/p>\n What frustrated her most was that accuracy didn\u2019t seem to be anyone\u2019s priority. Foundation model companies, she said, are \u201cextremely focused on coding and math,\u201d whereas news and information are harder. But harder, she argued, doesn\u2019t mean optional.<\/p>\n Indeed, when Forum AI began evaluating the leading models, the findings weren\u2019t exactly encouraging. She cited Gemini pulling from Chinese Communist Party websites \u201cfor stories that have nothing to do with China,\u201d and noted a left-leaning political bias across nearly all models. Subtler failures abound too, she said, including missing context, missing perspectives, straw-manning arguments without acknowledgment. \u201cThere\u2019s a long way to go,\u201d she said. \u201cBut I also think that there are some very easy fixes that would vastly improve the outcomes.\u201d<\/p>\n Brown spent years at Facebook watching what happens when a platform optimizes for the wrong thing. \u201cWe failed at a lot of the things we tried,\u201d 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 lousy for society and left many less informed.<\/p>\n 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 the idealistic version of that \u2014 AI optimizing for truth \u2014 might sound naive. But she thinks enterprise may be the 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.”<\/p>\n 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.<\/p>\n 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 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.”<\/p>\n Brown \u2014 whose company last fall raised $3 million<\/a> led by Lerer Hippeau \u2014 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.” <\/p>\n 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.”<\/p>\n<\/div>\n When you purchase through links in our articles, we may earn a small commission<\/a>. This doesn\u2019t affect our editorial independence.<\/em><\/p>\n