Expertise, Personal Experience, and Algorithm Aversion (study 4)

Last registered on June 21, 2022

Pre-Trial

Trial Information

General Information

Title
Expertise, Personal Experience, and Algorithm Aversion (study 4)
RCT ID
AEARCTR-0009610
Initial registration date
June 18, 2022

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
June 21, 2022, 8:40 AM EDT

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

Locations

Region

Primary Investigator

Affiliation

Other Primary Investigator(s)

PI Affiliation
Peking University
PI Affiliation
Peking University

Additional Trial Information

Status
Completed
Start date
2022-06-15
End date
2022-06-19
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
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, Cong Wang and Chong (Alex) Wang. 2022. "Expertise, Personal Experience, and Algorithm Aversion (study 4)." AEA RCT Registry. June 21. https://doi.org/10.1257/rct.9610
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2022-06-15
Intervention End Date
2022-06-19

Primary Outcomes

Primary Outcomes (end points)
Main outcome is reliance on AI after receiving different information about AI's relative performance compared to self.
Primary Outcomes (explanation)
If the initial diagnosis is different from AI's advice, and the subject changed her diagnosis to the AI's advice later, then reliance is 1, and otherwise 0.

Secondary Outcomes

Secondary Outcomes (end points)
their tolerance of the AI and a human expert
Secondary Outcomes (explanation)
To what extent do you agree with the following statement?
“If the AI underperformed me in its domain, although it rarely happens, I would think that the AI is not good enough”.
“If the expert underperformed me in his/her domain, although it rarely happens, I would think that the expert is not good enough”.
(1-5, from completely agree to completely disagree)

Experimental Design

Experimental Design
Each doctor will be randomly assigned to the aggregate information treatment or the control, where they will receive information regarding their performance in a diagnosis task relative to an AI. Then, subjects will receive advise from the AI. We will measure their reliance on AI’s advice.
Experimental Design Details
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
131 doctors per group. That is about 262 in total.
Sample size: planned number of observations
393 observations in each group
Sample size (or number of clusters) by treatment arms
We need at least 393 observations in each group. That is about 131 individuals per group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
pwr.t.test(n = , d = 0.2, sig.level = 0.05, power = 0.8, type = c("two.sample"))
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

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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