From Chaos to Clarity: A vision for AI, news, and complex topics

For the past several years, I’ve worked at the crossroads of news, AI, and social media; leading trust and safety efforts at Facebook, Instagram, and Meta AI, and advising smaller companies grappling with these same challenges. I've tackled issues ranging from COVID misinformation to racism, election integrity, and global conflicts at billion-user scale.

It’s no surprise to me that AI is already transforming how we navigate news and sensitive topics. The appeal is obvious. Faced with an endless stream of articles, social media posts, and claims, we've embraced AI as our filter: a single source of truth cutting through information chaos. No bylines, comment threads, or sourcing to worry about, just clean, seemingly authoritative answers to complex questions.

It feels like exactly the right solution at exactly the right time. 

But we're not solving our information crisis. We're masking it. The fundamental challenge hasn't disappeared, it's simply been transferred to machines that are even less equipped to handle it than we are. AI systems need to parse the same chaotic mix of perspectives, the same incomplete coverage, the same conflicting narratives that overwhelm human readers.

Consider what happens when an AI system encounters competing narratives about economic policy, international conflicts, or public health measures. We can instruct the AI to "be balanced and accurate," but without proper tools, these commands are meaningless. It's like asking someone to cook a gourmet meal blindfolded with no recipe books.

In practice, this leads to inadequate outputs: while AI systems excel at code and problem solving, they make errors when talking about news over 50% of the time.

AI systems need maps, not just directions. 

The path forward requires translating our complex news ecosystem for AI. We can’t just feed news sources to the AI and hope it does a good job, we have to give it the context it needs to prioritize, interpret, and talk about that news. But how do we do this? We could cross our fingers and let AI make these decisions itself? We could trust a private company to do this in a black box?

The right answer is to empower credible, trusted experts to design this future. 

We need economists explaining how they evaluate policy coverage. We need regional specialists identifying when translation choices obscure cultural context. We need scientists distinguishing between preliminary findings and robust consensus or between industry-funded research and independent studies. 

→ This information can help us build “News Labelers” that add the necessary context to sources so that AI systems can select, prioritize, and interpret them.

We need experts to determine how AI systems should talk about the news. When should they acknowledge uncertainty? How do they distinguish fact from opinion? What terminology should be used or avoided? How should this differ across economics, geopolitics, and sports?

→ This information can help us set “Communication Standards” for how we instruct AI systems to communicate accurately and transparently about news.

We also need domain experts to tell us what “good” looks like. What makes an answer better or worse? What is the relative importance of depth vs. clarity across topics? How does this differ across verticals?

→ This information can help us establish “Evaluation Criteria” that the industry can use to measure and improve upon our systems. We can even use this to establish public benchmarks rooted in expertise.

While the prospect of AI transforming news feels threatening, it's actually our best opportunity to fix information problems that predate these technologies. Today we're drowning in conflicting sources, trapped in echo chambers, and overwhelmed by the volume of information demanding our attention. 

If we can work with domain experts to build the right foundation—including News Labelers that provide crucial context, Communication Standards that ensure transparent communication, and Evaluation Criteria rooted in genuine expertise—we can create AI systems that genuinely improve upon our news ecosystem rather than simply automating our confusion.