World’s First AI Personas Based on Real American Voters Shape the Presidential Election Conversation.

Friday November 1st

As we approach another pivotal election season, RehabAI is excited to unveil a new frontier in election analysis: AI-powered synthetic personas, the first-ever tool designed to capture and predict U.S. voter sentiment and behavior in real-time. Developed in partnership with Sundogs, these personas go beyond traditional polling and data analytics by simulating how diverse voter segments engage with news, campaign ads, and critical issues.

By combining 75 years of historical election data, U.S. Census information, and socio-political insights from Pew Research Center, our synthetic personas offer an unparalleled perspective on the regional beliefs and societal attitudes that shape the American electorate. Here, we’ll dive deeper into the development process, the data foundation, the ethical considerations, and the implications of these AI personas for the future of election analysis.

The Concept: Modeling Real Conversations in a Bellwether County

Our synthetic personas simulate a fictional bellwether county in Pennsylvania—a critical state known for influencing election outcomes due to its mix of urban, suburban, and rural populations. By replicating voter conversations and value-based decision-making within this county, these personas capture the nuanced perspectives that define U.S. voting behavior. This approach allows for a more dynamic, contextual understanding of voter sentiment, grounded in regional and cultural influences that polling data alone often cannot fully represent.

Building Synthetic Personas: A Three-Layered Data Approach

Creating personas that authentically represent American voters required a comprehensive, multi-layered approach to data:

1. Foundational Layer: The foundation of each persona is built on U.S. Census data, historical voting patterns, and American National Election Studies (ANES) data. This layer defines essential demographic attributes such as age, race, income, education level, and political affiliation, ensuring that each persona is rooted in factual, representative data. By incorporating 75 years of election data, this foundational layer captures long-term trends in voting behavior and demographic shifts.

2. Enrichment Layer: The enrichment layer incorporates additional social and attitudinal insights from Pew Research Center studies. This layer adds depth to the personas by reflecting specific social trends, values, and emerging political preferences, including:

   • Generational shifts in support for multi-party systems.

   • Religious perspectives on immigration.

   • Voting behaviors within different racial and cultural demographics, such as Black, Asian American, and white Protestant voters.

   • Societal attitudes on contemporary issues like gender equity, economic inequality, and educational priorities.

3. Qualitative Research Layer: To make these personas even more relatable and grounded in real-world perspectives, we conducted a qualitative research phase. This layer brings in emotional and behavioral nuances, capturing trends like political dissatisfaction among younger voters, economic concerns in specific regions, and religious divides on issues like healthcare and immigration. By adding these subtleties, the personas provide a more textured view of voter motivations and value-based decision-making.

From Data to Dynamic Personas: Transforming Insights into LLM-Driven Models

After gathering and layering the data, our team at RehabAI used a Large Language Model (LLM) to turn raw insights into fully developed voter personas. This process involved synthesizing demographic, psychological, and behavioral information into detailed profiles that accurately mirror the values, concerns, and priorities of different voter segments across the U.S. This LLM-driven approach enables our personas to adapt dynamically to current events and real-time news, providing a constantly evolving picture of voter sentiment.

A key example of this adaptive approach is our detailed study of Door County, where we segmented the population into sixteen unique voter personas based on local demographic and voting data. By identifying key swing voters with specific concerns around healthcare access and support for small businesses, the model provides insights into how localized issues influence voter decisions and, ultimately, election outcomes.

A Flexible, Iterative Model with Hyper-Local Precision

One of the strengths of our synthetic personas is their flexibility. This is an iterative model that continuously incorporates new data sources, allowing us to refine the personas as the political landscape shifts. Localized data, such as voter turnout and county-specific issues, further enhance the personas, making them accurate reflections of real-world behaviors and beliefs in specific areas.

By using hyper-local insights and continuously updating the data, RehabAI’s personas are not only reactive to the latest news but also predictive of evolving trends in voter sentiment. This approach allows political campaigns, media organizations, and researchers to tap into the nuanced preferences of different voter segments, from large national trends to the granular concerns of a single county.

Ethics in AI: Ensuring Unbiased, Representative Personas

At RehabAI, ethics is a cornerstone of our work. We recognize the potential impact of synthetic personas on election analysis and are committed to maintaining high ethical standards. Our approach prioritizes a balanced and representative dataset to ensure that the personas authentically reflect the diversity of the U.S. electorate without introducing additional bias. Every data source is carefully vetted to ensure inclusivity across demographics, perspectives, and social attitudes.

This ethical commitment means that our personas provide an objective, transparent view of voter sentiment, allowing campaigns and media outlets to engage with these insights responsibly. By mirroring the electorate’s diversity and providing balanced perspectives, RehabAI’s synthetic personas support an unbiased, responsible approach to election analysis that avoids skewed insights and promotes fairness.

Real-World Applications: The Future of Election Analysis

RehabAI’s synthetic personas represent a significant leap forward in the field of political analysis. Here’s how these personas are already making an impact:

• Real-Time Reactions: Personas dynamically adjust to news and events, offering immediate feedback on shifts in voter sentiment.

• Campaign Ad Analysis: By simulating voter reactions, the personas assess the effectiveness of campaign ads, pinpointing messages that resonate with different segments.

• Predictive Accuracy: By combining historical data with real-time analysis, these personas provide highly accurate predictions of election outcomes and voter behavior.

• Diverse Representation: The personas capture the complex makeup of the electorate, including regional nuances and diverse voter identities, ensuring a comprehensive view of the factors influencing voter decisions.

To learn more about the research behind our synthetic personas and how they’re changing the election landscape, book some time with our teams.