Expertise, Personal Experience and Algorithm Aversion: Study 3

Last registered on October 20, 2021


Trial Information

General Information

Expertise, Personal Experience and Algorithm Aversion: Study 3
Initial registration date
October 18, 2021

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
October 20, 2021, 1:42 PM EDT

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



Primary Investigator


Other Primary Investigator(s)

PI Affiliation
Peking University
PI Affiliation
Peking University

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Artificial Intelligence (AI) outperforms humans in many tasks. However, accumulating evidence suggests AI aversion – a tendency to distrust and thus ignore AI advice, especially among experts. In a two-stage experiment, we test if trained experts are more acceptive of AI advice in forming predictions and are more sensitive to the AI’s incidental performance. Our results will help design algorithms that are better adopted by human decision-makers, and mitigate the biases that experts hold on algorithms.
External Link(s)

Registration Citation

Gao, Yu, Chong (Alex) Wang and Cong Wang. 2021. "Expertise, Personal Experience and Algorithm Aversion: Study 3." AEA RCT Registry. October 20.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
change in WOA
Primary Outcomes (explanation)
Weight on advice: the difference between the initial and revised judgment divided by the difference between the initial judgment and advice. WOA of 0% occurs when a participant ignores advice and WOA of 100% occurs when a participant abandons his or her prior
judgment to match the advice.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In Stage 1, each subject will be randomly assigned to a training task to predict exam scores according to application profiles or to predict celebrities’ BMI according to their face photos. During the training phase, subjects will receive immediate feedback regarding their performance in the corresponding prediction task.
After the training phase, we measure subjects’ baseline weight on advice (WOA) using a different prediction task. In specific, if a subject was trained using the exam score (BMI) task, then we measure her WOA using the BMI (exam score) task.
In Stage 2, each subject will be exposed to three randomly drawn levels of AI’s performance using the strategy method approach. We then measure the endline weight on advice (WOA).
Experimental Design Details
Randomization Method
Randomization will be done by the survey platform.
Randomization Unit
In stage 1, randomization is done at the individual level.
In stage 2, each individual will be assigned into a positive or negative performance condition, and then we draw three feedback levels in each corresponding condition.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
352 individuals per group
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
at least 352 individuals in training 1, and 352 individuals in training 2.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
pwr.f2.test(u=3,v=,f2=0.02,sig.level=0.05,power=0.9) pwr.f2.test(u=3,v=,f2=0.02,sig.level=0.01,power=0.9)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials