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Expertise, Personal Experience and Algorithm Aversion
Last registered on May 02, 2021

Pre-Trial

Trial Information
General Information
Title
Expertise, Personal Experience and Algorithm Aversion
RCT ID
AEARCTR-0007005
Initial registration date
January 11, 2021
Last updated
May 02, 2021 10:54 PM EDT
Location(s)
Region
Primary Investigator
Affiliation
Other Primary Investigator(s)
PI Affiliation
Peking University
PI Affiliation
Peking University
Additional Trial Information
Status
Completed
Start date
2021-01-12
End date
2021-02-28
Secondary IDs
Abstract
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
Citation
Gao, Yu, Chong (Alex) Wang and Cong Wang. 2021. "Expertise, Personal Experience and Algorithm Aversion." AEA RCT Registry. May 02. https://doi.org/10.1257/rct.7005-2.0.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2021-01-12
Intervention End Date
2021-02-28
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
individual
Was the treatment clustered?
No
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
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
PKU GSM-IRB
IRB Approval Date
2021-01-12
IRB Approval Number
2021-05
Analysis Plan

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Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is 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