AI and Digital Influence in Elections

AI and Digital Influence in Elections

Overview

Discussing prediction markets, digital influence methods, ethics and standards, and election coverage ahead of the 2026 midterms.

The Tow Center for Digital Journalism is hosting a one day conference to encourage conversations between journalists and researchers as we navigate the ever-changing and chaotic information landscape leading up to the 2026 midterms. Sessions will cover the current state of AI’s role in electoral politics, current digital influence methods, the promise and limits of predictive analytics, and the practical standards newsrooms need when the tools shaping information are moving faster than the institutions meant to check them.

Registration closes 24 hours before event start.

Lineup

Emily Bell

Renée DiResta

Julia Angwin

C.J. Robinson

Dr. Anya Schiffrin

Madison Karas

Good to know

Highlights

  • 6 hours
  • In person

Location

Pulitzer Hall

2950 Broadway

New York, NY 10027

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Map

Agenda

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AI and Elections Coverage

Emily Bell, Renee DiResta, Julia Angwin

AI-generated content spreads while newsroom staffing shrinks. That means fewer journalists are left to track an information movement that’s constantly shifting. This panel takes stock of where AI and electoral politics currently stand. The panelists will look at a broad view of how newsrooms are covering elections in light of increased information influence campaigns, how newsrooms can improve their coverage, and what journalists and scholars can expect from the midterms and beyond

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Reporting on Digital Influence Methods

C.J. Robinson, Anya Schiffrin, Madison Karas, Maxwell Howard

Anyone who wants to influence public opinion and election discourse can get an influence operation up and running within minutes at very little cost. Understanding the mechanics behind these influence methods is vital. Panelists will talk about the blurred lines between authentic and manufactured discourse, tracing how digital influence campaigns spread and evolve over time.

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Lunch

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