Sho Miyazaki (Waseda University), "Why Do AI Models Tell Left-Wing Voters to Support the Communist Party? AI Voting Advice in Japan’s 2026 General Election"
2026-05-21 20:00 - 21:00 (America/New_York)
US ET: May 21 (Thursday), 8 – 9 PM
JST: May 22 (Friday), 9 – 10 AM
Zoom Registration: Link
Paper: Link
Authors: Sho Miyazaki (Waseda University), Andrew B. Hall (Stanford University)
Presenter: Sho Miyazaki (Waseda University)
Abstract:
Al chatbots with web search are increasingly used for voting guidance, yet we know relatively little about their recommendation behavior outside the US. We query five models from three companies with 36,300 synthetic voter profiles during Japan’s 2026 Lower House election, plus a follow-up with today’s four frontier models. Policy stances dominate recommendations, producing 50-98 percentage point swings compared to 0.5-7.0 pp for demographics. Strikingly, left-leaning stances cause all five models to converge on Japan’s Communist Party, despite other parties holding similar positions on the relevant issues. We trace this to the information environment: JCP operates a fully open website with a party newspaper that models freely access and frequently misclassify as independent journalism, while major Japanese news outlets block Al crawlers. Without policy input, models show no uniform left-wing bias. Incorporating X search shifts recommendations modestly leftward, contradicting US-centric expectations. These findings suggest that Al models need better source discrimination in non-US contexts, and that news organizations blocking Al access may inadvertently cede influence over Al-mediated voting advice to partisan sources that remain open.
Discussants: Kentaro Fukumoto (University of Tokyo), Kenneth McElwain (University of Tokyo)
Chair: Yusaku Horiuchi (Florida State University)
