Expertise, Personal Experience and Algorithm Aversion

Last registered on May 02, 2021


Trial Information

General Information

Expertise, Personal Experience and Algorithm Aversion
Initial registration date
January 11, 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
January 11, 2021, 6:55 AM EST

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

Last updated
May 02, 2021, 10:54 PM EDT

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



Primary Investigator


Other Primary Investigator(s)

PI Affiliation
Peking University
PI Affiliation
Peking University

Additional Trial Information

Start date
End date
Secondary IDs
Research shows that evidence-based algorithms perform better than humans in predicting the future. Yet people give less weight to AI advice than they should. By exogenously variating personal experience with AI predictions, we explore how personal experience impacts weight on algorithm advice and how the level of expertise moderates this relationship. 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." AEA RCT Registry. May 02.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Weight on advice
Confidence in self
Confidence in algorithm
The belief in own accuracy
The belief in AI accuracy

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
We will randomly variate each subject's personal experience with AI.
Experimental Design Details
Relative performance between self and AI:
+6,+12, +24, -6, -12, -24, 0 (for the pilot)

For the formal study, we only keep +12, +24, -12, -24, 0 as our pilot study showed that +6 and -6 had little effects on our results.
Randomization Method
Randomization will be done by the survey platform.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
50 per treatment

According to our pilot test based on N=50 per treatment, the mean in changes in WOA is 0.0227, the standard deviation is 0.2453. We calculate the number of subjects needed to detect the difference with power 0.8 and significance level of 0.1 and conclude that we need 720 subjects in total, or 720/7=103 subjects per treatment.
Sample size: planned number of observations
50*7=350 subjects Our pilot study was based on the pre-specified 350 subjects. Then our formal study was based on n=100 per treatment, as calculated above.
Sample size (or number of clusters) by treatment arms
50*7=350 subjects for the pilot

100*5(variation of feedback)*2(experts vs. layman)=1000 subjects for the formal study
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
pwr.t.test(n = , d = mean(delta_WOA)/sd(delta_WOA), sig.level = 0.1, power = 0.8, type="paired") where mean(delta_WOA)=-0.0227, sd(delta_WOA)=0.2453

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