Between October 30 and November 2, 2025, our voice AI system conducted 43 semi-structured interviews with constituents located in the 5 boroughs. The AI asked about party identification, voting intentions, candidate support, policy priorities, information sources, and concerns about the city. It followed up on answers, asked for clarification, and let conversations extend when respondents wanted to elaborate. Constituents accessed the interview through a web link, spoke naturally, and ended the conversation when finished. Interview lengths varied depending on constituents’ willingness to share, but ran as long as 30+ minutes.
These are excerpts from 43 conversations between New York City voters and the Third Ear voice AI interviewer during the final days of the 2025 mayoral election. The interviews averaged 14 minutes each and generated thousands of words of detailed constituent perspectives. We conducted this study to test whether a goal-directed, semi-structured autonomous voice AI could collect the kind of nuanced, qualitative political data that traditionally requires expensive and time-intensive human interviewers.
What Constituents Actually Said
The Housing Affordability Crisis is a Top-of-Ballot Issue
Roughly 76 percent of respondents raised housing affordability without being prompted about it. They didn't just mention it, they explained it in vivid detail.
These quotes come from different interviews, but they share a common thread: housing isn't an abstract policy issue for these voters. It's a daily constraint that shapes where they live, how they work, and whether they stay in the city.
Safety Concerns Are Split Along Multiple Lines
Public safety appeared in 60 percent of interviews, but perspectives diverged in ways that simple polling can't capture.
Some respondents wanted more police presence:
Others felt over-policed:
Still others focused on mental health:
These aren't contradictory views, but reveal different people experiencing different aspects of urban safety. A survey asking multiple choice questions would miss these distinctions entirely.
