Humans, AI, and the Trouble with Truth: A Conversation with Dean Eckles

The CX Iconoclast

In this episode of CX Iconoclast, Richard Owen sits down with Dean Eckles, MIT professor and former Facebook data scientist, to explore the real-world dynamics of misinformation—and what actually works to stop it. Eckles shares insights from his landmark research showing that correcting false content on social media doesn’t always reduce misinformation. In fact, it can sometimes make things worse, prompting users to double down and share even more toxic content. But there’s good news: features like Community Notes, when designed to provide neutral context from a broad base of users, can significantly reduce the viral spread of low-quality information—especially among more passive, less entrenched audiences.

The conversation shifts to how businesses can close the data gap. Eckles describes how platforms like Meta leverage rich behavioral data to adjust for survey bias, offering a blueprint for companies that want more accurate customer insights. But most organizations aren’t sitting on massive data lakes—and that’s where predictive AI and synthetic data can help fill in the blanks. Richard and Dean also tackle a key leadership challenge: how to get executives to trust data over instinct. The goal? Not perfection, but progress. Use AI to narrow the decision space, eliminate high-risk choices, and create meaningful lift—whether you’re in the boardroom or on the front lines. It’s not about man versus machine. It’s about smarter decisions, powered by both.

Dean Eckles is a social scientist and statistician. At the Massachusetts Institute of Technology, Dean is the William F. Pounds Professor in the MIT Sloan School of Management and associate director of the Institute for Data, Systems & Society in the Schwarzman College of Computing. Much of his research examines how interactive technologies affect human behavior, especially by mediating social influence. Dean also works on methods for inferring cause–effect relationships and on applied statistics more generally. Dean’s empirical work uses observational studies and field experiments involving hundreds of millions of people.

His papers appear in Journal of the American Statistical Association, Proceedings of the National Academy of Sciences, Nature, Operations Research, Management Science, and other peer-reviewed journals and proceedings in statistics, computer science, and marketing. He is a co-organizer of the Conference on Digital Experimentation (CODE@MIT). He was previously a scientist at Facebook and Nokia. Dean completed five degrees, including his PhD, at Stanford University.

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