Knowledge about Artificial Intelligence (AI) and appraisal ratings: How people assess AI-generated images.

Last registered on March 27, 2020

Pre-Trial

Trial Information

General Information

Title
Knowledge about Artificial Intelligence (AI) and appraisal ratings: How people assess AI-generated images.
RCT ID
AEARCTR-0005065
Initial registration date
November 26, 2019

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
November 26, 2019, 10:38 AM EST

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

Last updated
March 27, 2020, 5:05 AM EDT

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

Locations

Region

Primary Investigator

Affiliation

Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

Status
In development
Start date
2019-10-10
End date
2020-03-31
Secondary IDs
Abstract
Through an online experiment on Amazon's Mechanical Turk, we examine how people assess images generated by artificial intelligence (AI) in order to gain more insights into how people perceive AI. By using three different information sets at the beginning of the survey, subjects are confronted with images generated by AI and rate them according to liking, emotion, creativity, and hypothetical willingness to pay. Finally, the evaluations of the AI-generated images of the treatment groups will be compared with a control group that receives no information.
External Link(s)

Registration Citation

Citation
Hauser, David et al. 2020. "Knowledge about Artificial Intelligence (AI) and appraisal ratings: How people assess AI-generated images.." AEA RCT Registry. March 27. https://doi.org/10.1257/rct.5065
Experimental Details

Interventions

Intervention(s)
In a between-subject design, participants are asked to rate six images all created by artificial intelligence (AI) regarding liking, emotion, creativity, and hypothetical willingness to pay. We aim to examine whether subjects assess images generated by an AI differently when subjects:
i.) have no information how an image was created;
ii.) know that an image was created by an AI;
iii.) know that an image was created by an AI and also receive basic information about how the AI creates an image;
iv.) know that an image was created by an AI and also receive information about the person and team who programmed the AI.
Intervention Start Date
2019-11-26
Intervention End Date
2019-12-03

Primary Outcomes

Primary Outcomes (end points)
- Evaluation of the general liking of the images on a scale from 0 to 100.
- Evaluations about experienced emotions, whether the subject experienced that the picture is communicating with him/her and the creativity and of the images on a 7-point Likert scale.
- Hypothetical willingness to pay for the images.
- Basic demographical metadata.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Between-subject design.
Experimental Design Details
Randomization Method
randomization done in office by a computer.
Randomization Unit
Individual.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
0.
Sample size: planned number of observations
In total around 560 participants.
Sample size (or number of clusters) by treatment arms
Around 140 participants per treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Ethikkommission
IRB Approval Date
2019-11-25
IRB Approval Number
142019

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials