Artificial Intelligence and Political Polarization

Last registered on August 27, 2026
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Pre-Trial

Trial Information

General Information

Title
Artificial Intelligence and Political Polarization
RCT ID
AEARCTR-0019460
Initial registration date
August 20, 2026

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
August 27, 2026, 11:46 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Name
Siyu Wang
Affiliation
Villanova University
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Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2026-08-19
End date
2026-11-01
Keywords
Behavior
Additional Keywords
Political Polarization, Artificial Intelligence
JEL code(s)
D91, O33, Z13, C91
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study examines whether individuals’ responses to artificial intelligence depend on the identity dimension through which they interpret technological change. Motivated by a two-dimensional framework in which individuals can identify with an economic group or a cultural group, participants first report their expectations about the consequences of AI and are then randomly assigned to reflect on their economic identity, cultural identity, or a neutral identity. We examine whether inducing identity along different dimensions changes AI-related opinions and donation allocations across organizations addressing different economic or cultural consequences.
External Link(s)

Registration Citation

Citation
Wang, Siyu. 2026. "Artificial Intelligence and Political Polarization." AEA RCT Registry. August 27. https://doi.org/10.1257/rct.19460-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Participants are randomly assigned to one of three treatments that differ in the identity dimension on which they are asked to reflect. In the economic-identity treatment, participants identify with an economic group and reflect on experiences associated with that group. In the cultural-identity treatment, participants identify with a cultural group and reflect on experiences associated with that group. In the neutral-identity treatment, participants identify as either a cat person or a dog person and complete corresponding reflection questions. The interventions are designed to vary the identity dimension through which participants evaluate the consequences of artificial intelligence.
Intervention Start Date
2026-08-21
Intervention End Date
2026-11-01

Primary Outcomes

Primary Outcomes (end points)
Participants’ donation allocation of $100 across four organizations addressing different economic and social consequences.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Participants’ opinions across four economic and cultural dimensions: economic inequality, job retraining, national security, and discrimination.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study is a randomized online experiment with three treatments. Participants first report expectations about several consequences of artificial intelligence. They are then randomly assigned to reflect on their economic identity, their cultural identity, or a neutral identity. Following the intervention, all participants answer the same questions concerning AI-opinions and complete a donation-allocation task. The design allows us to examine whether responses to technological change depend on the identity dimension through which individuals are induced to evaluate it.
Experimental Design Details
Not available
Randomization Method
Randomization is conducted by computer using the randomization function in Qualtrics. Participants will be randomly assigned to the neutral-identity, economic-identity, or cultural-identity treatments at individual level.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No cluster. The treatment is randomized at individual level.
Sample size: planned number of observations
600-800 participants
Sample size (or number of clusters) by treatment arms
200-267 per treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We plan to recruit between 600 and 800 participants, with approximately equal assignment across three treatment conditions. With 600 participants, or approximately 200 participants per treatment arm, the study has approximately 80% power at a two-sided 5% significance level to detect pairwise treatment effects of approximately 0.28 standard deviations. With 800 participants, or approximately 267 participants per treatment arm, the corresponding minimum detectable effect is approximately 0.24 standard deviations. Thus, the planned sample is designed to detect treatment effects in the range of approximately 0.24–0.28 standard deviations.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Villanova University Institutional Review Board
IRB Approval Date
2026-04-29
IRB Approval Number
IRB #2026-214
Analysis Plan

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