Expertise, Personal Experience and Algorithm Aversion

Last registered on May 02, 2021

View Trial History

Pre-Trial

Trial Information

General Information

Title

Expertise, Personal Experience and Algorithm Aversion

RCT ID

AEARCTR-0007005

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.

Locations

Country

China

Region

Primary Investigator

Name

Yu Gao

Affiliation

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Chong (Alex) Wang

PI Affiliation

Peking University

Contact Investigator

PI Name

Cong Wang

PI Affiliation

Peking University

Contact Investigator

Additional Trial Information

Status

Completed

Start date

2021-01-12

End date

2021-02-28

Keywords

Behavior, Lab

Additional Keywords

Algorithm Aversion, Decision from Experience

JEL code(s)

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

Sponsors & Partners

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

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?

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

Supporting Documents and Materials

IRB