No Bias, No Bull AI I’ve spent my career grappling with bias. As an executive at Meta overseeing news and fact-checking, I saw how algorithms and AI systems shape what billions of people see and believe. As a journalist at CNN, I even hosted a show briefly called “No Bias, No Bull”(easier said than done, as it turned out). Trump’s executive order on “woke AI” has reignited debate around bias and AI. The implication was clear: AI systems aren’t just tools, they’re new media institutions, and the people behind them can shape public opinion as much as any newsroom ever did. But for me, the real concern isn’t whether AI skews left or right, it’s seeing my teenagers use AI for everything from homework to news without ever questioning where the information comes from. Political bias misses the deeper issue: transparency. We rarely see which sources shaped an answer, and when links do appear, most people ignore them. An AI answer about the economy, healthcare, or politics, sounds authoritative. Even when sources are provided, they’re often just footnotes while the AI presents itself as the expert. Users trust the AI’s synthesis without engaging sources, whether the material came from a peer-reviewed study or a Reddit thread. And the stakes are rising. News-focused interactions with ChatGPT surged 212% between January 2024 and May 2025, while 69% of news searches now end without clicking to the original claiming neutrality while harboring clear bias. We’re making the same mistake with AI, accepting its conclusions without understanding their origins or how sources shaped the final answer. The solution isn’t eliminating bias (impossible), but making it visible. Restoring trust requires acknowledging everyone has perspective, and pretending otherwise destroys credibility. AI offers a chance to rebuild trust through transparency, not by claiming neutrality, but by showing its work. What if AI didn’t just provide sources as afterthoughts, but made them central to every response, both what they say and how they differ: “A 2024 MIT study funded by the National Science Foundation…” or “How a Wall Street economist, a labor union researcher, and a Fed official each interpret the numbers…”. Even this basic sourcing adds essential context. Some models have made progress on attribution, but we need audit trails that show us where the words came from, and how they shaped the answer. When anyone can sound authoritative, radical transparency isn’t just ethical, it’s the principle that should guide how we build these tools. What would make you click on AI sources instead of just trusting the summary? Full transparency: I’m developing a project focused precisely on this challenge– building transparency and attribution into AI-generated content. Love your thoughts.

- Campbell Brown.