AI Misinformation and Trust in News

Last registered on February 24, 2025
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Pre-Trial

Trial Information

General Information

Title
AI Misinformation and Trust in News
RCT ID
AEARCTR-0015359
Initial registration date
February 12, 2025

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
February 19, 2025, 8:43 AM EST

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

Last updated
February 24, 2025, 10:42 AM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

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Primary Investigator

Name
Ananya Sen
Affiliation
Carnegie Mellon University
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Other Primary Investigator(s)

PI Name
Filipe Campante
PI Affiliation
Johns Hopkins University
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PI Name
Ruben Durante
PI Affiliation
National University of Singapore
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PI Name
Felix Hagemeister
PI Affiliation
Süddeutsche Zeitung
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Additional Trial Information

Status
In development
Start date
2025-02-22
End date
2025-04-22
Keywords
Firms & Productivity, Other
Additional Keywords
AI, News Media, Trust, Misinformation, Field Experiment
JEL code(s)
D91, L82
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study aims to understand the extent to which technology-driven misinformation can alter institutional trust. In particular, we focus on how AI-generated misinformation can increase or decrease trust in media outlets. We partner with a large news outlet in Germany to test this by asking users to take a survey. These surveys are a standard part of the news outlet's way of doing consumer research. We randomly assign online users on the website into treatment and control groups within one such survey. In the treatment group, we show them three pairs of pictures sequentially and ask them to guess which one is AI-generated and which one is real. In the control group, we show them similar pairs of real pictures but ask them questions unrelated to AI. We then elicit their subjective beliefs about the importance of misinformation and their trust in our partner news outlet and other media platforms. We will also track their browsing and subscription behavior after the experiment.
External Link(s)

Registration Citation

Citation
Campante, Filipe et al. 2025. "AI Misinformation and Trust in News." AEA RCT Registry. February 24. https://doi.org/10.1257/rct.15359-1.1
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Experimental Details

Interventions

Intervention(s)
We randomly assign online users on the news website into treatment and control groups within one survey. In the treatment group, we show them three pairs of pictures sequentially and ask them to guess which one is AI-generated and which one is real. In the control group, we show them similar pairs of pictures but ask them questions unrelated to AI. We will swap out the AI generated picture with similar real ones in the control group.
Intervention Start Date
2025-02-22
Intervention End Date
2025-04-22

Primary Outcomes

Primary Outcomes (end points)
(1) Subjective beliefs about trust in media and the importance of misinformation
(2) Preference for subscribing to the news outlet.
(3) Longer-term browsing and subscription behavior
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We randomly assign online users on the news website into treatment and control groups within one survey. In the treatment group, we show them three pairs of pictures sequentially and ask them to guess which one is AI-generated and which one is real. In the control group, we show them similar pairs of pictures but ask them questions unrelated to AI. We will swap out the AI generated picture with similar real ones in the control group.

We will have a between-subject design such that an individual will be asked to complete a survey quiz only once and will be assigned to only one of the two groups. The main outcome variables of interest will be to look at subjective beliefs post the survey, browsing and clicking behavior on the website in the short and medium run.

Apart from these baseline treatment effects, we will also analyze heterogeneity on certain dimensions. We will analyze how the treatment effect varies by type of online visitor (such as subscriber/non-subscriber, heavy vs. light user) and how well the individual did on the quiz. A priori, the direction of the effect along these dimensions is ambiguous.
Experimental Design Details
Not available
Randomization Method
Randomization will be done with a computer software.
Randomization Unit
Individual level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
--
Sample size: planned number of observations
20,000 -- this could be subject to some change based on survey uptake and traffic allocation by the company.
Sample size (or number of clusters) by treatment arms
10,000 in treatment and 10,000 in control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan