Can Math Save Journalism?: Julia Angwin on Proof, Power, and Amazon's Algorithm
About this episode
Julia Angwin discusses the intersection of journalism and mathematics, emphasizing the importance of data-driven investigations in combating authoritarianism.
In this conversation we chat with Julia Angwin — Pulitzer Prize-winning journalist, founder of Proof News, and former Wall Street Journal and ProPublica reporter — to make the case that journalism should function more like mathematical proof than anecdote. We cover how Angwin's team at The Markup used a decision-tree model to prove Amazon was favoring its own products in search results by an 8-to-1 margin — a finding the House Antitrust Committee later cited when referring Amazon to the DOJ for possible perjury. We dig into her "ingredients label" approach to reporting at Proof News (hypothesis, sample size, techniques, limitations), the difference between mathematical proof and the scientific method, and why she thinks control over algorithmic media is now the central battleground for authoritarian power. She also unpacks her new book on resisting authoritarianism, built from interviews with dissidents worldwide, including the "Swiss cheese" model of personal security and why perfectionism is dangerous in a crisis. Chapters 09:50 Proof News: A New Era in Journalism 19:56 Data-Driven Investigations: A Case Study 30:02 The Future of Journalism and AI 32:53 The Evolution of Search…
More episodes of Breaking Math Podcast
- Why Nothing Works: Robber Barons, Algorithms & Governing AI
- The Proof in the Code: How Lean Is Quietly Rewriting Trust in Math (w/ Kevin Hartnett)
Explore listener stats, chart rankings, contacts and more on the Breaking Math Podcast podcast page.