Using Housing Photos to Understand Trust in AI

Last registered on March 05, 2026

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Pre-Trial

Trial Information

General Information

Title

Using Housing Photos to Understand Trust in AI

RCT ID

AEARCTR-0018007

Initial registration date

February 26, 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

March 05, 2026, 6:47 AM EST

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Angel Tsai-Hsuan Chung

Affiliation

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Mariaflavia Harari

PI Affiliation

Wharton Real Estate Department

Contact Investigator

PI Name

Maisy Wong

PI Affiliation

Wharton Real Estate Department

Contact Investigator

Additional Trial Information

Status

Completed

Start date

2026-02-25

End date

2026-02-28

Keywords

Behavior, Other

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

This study examines how people develop trust in AI-driven resource allocation systems leveraging housing photos as a context. Using a hypothetical scholarship allocation scenario that uses AI to rank applicants based on photos of their house, we explore how the photos-based approach can help people understand both AI’s strengths and potential biases. We investigate how different types of AI errors affect trust across participants from varying socio-economic backgrounds. This work provides evidence-based insights for organizations considering AI adoption in high-stakes resource allocation decisions.

External Link(s)

Registration Citation

Citation

Chung, Angel Tsai-Hsuan, Mariaflavia Harari and Maisy Wong. 2026. "Using Housing Photos to Understand Trust in AI." AEA RCT Registry. March 05. https://doi.org/10.1257/rct.18007-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention (Hidden)

Intervention Start Date

2026-02-25

Intervention End Date

2026-02-28

Primary Outcomes

Primary Outcomes (end points)

1. Based on this additional information, how likely are you to recommend that University U expands the piloting of this AI tool for scholarship decisions?
2. The AI ranks are accurate (5-point agreement scale)
3. I am confident in the AI tool’s rankings (5-point agreement scale)
4. The AI tool may seriously misjudge some applicants (5-point agreement scale).
5. The AI tool can ensure wealthy applicants with low reported income are excluded (5-point agreement scale).
6. The AI tool can ensure all poor applicants are included (5-point agreement scale).
7. Using the AI tool is fair (5-point agreement scale).

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

1. Consistency: AI applies the same criteria to every application (5-point importance scale).
2. Scalability: AI can assess many applicants at low cost (5-point importance scale).
3. Visual cues: AI can extract useful information from photos not reflected in application forms (5-point importance scale).
4. Irrelevant cues: AI may be misled by lighting or photo quality (5-point importance scale).
5. Use of neighborhood information: Only the applicant's home should be considered, and not the surroundings (5-point importance scale).
6. Privacy: Using publicly available Street View photos may give AI excessive access to personal data (5-point importance scale).
7. Would you be willing to have your own home evaluated by this AI tool as part of a financial-aid process?

Secondary Outcomes (explanation)

Experimental Design

We plan to administer a survey where we show respondents various examples of how a hypothetical AI-based tool can rank housing imagery to assess socio-economic status and we ask respondents about their perceptions.
After completing a baseline survey administered to all participants, respondents will be randomly assigned to one of four arms: one control arm and three treatment arms (see below). Within each arm, respondents will view two images along with ground truth rankings. The sequence in which these two images are presented will be randomized at the respondent level.

Experimental Design Details

Randomization Method

After completing a baseline survey administered to all participants, respondents will be randomly assigned to one of four arms: one control arm and three treatment arms (see below). Within each arm, respondents will view two images along with ground truth rankings as shown above. The sequence in which these two images are presented will be randomized at the respondent level.

Randomization Unit

Unit of randomization: individual.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

Treatment is not clustered.

Sample size: planned number of observations

600 students

Sample size (or number of clusters) by treatment arms

We will aim to allocate participants evenly across study arms. With one control arm and three treatment arms, each arm will comprise 25% of the overall sample.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

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