Coarsening Signals in Human-AI Interaction - Experiment 1

Last registered on July 03, 2024
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Pre-Trial

Trial Information

General Information

Title
Coarsening Signals in Human-AI Interaction - Experiment 1
RCT ID
AEARCTR-0013716
Initial registration date
May 30, 2024

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
May 30, 2024, 5:51 AM EDT

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

Last updated
July 03, 2024, 11:09 AM EDT

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

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Primary Investigator

Name
Ruru (Juan Ru) Hoong
Affiliation
Harvard University
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Other Primary Investigator(s)

PI Name
Bnaya Dreyfuss
PI Affiliation
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Additional Trial Information

Status
In development
Start date
2024-05-30
End date
2024-12-31
Keywords
Finance & Microfinance, Firms & Productivity, Labor
Additional Keywords
AI, decision-making
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Artificial intelligence (AI) has caught pace with — and in some contexts even surpassed — humans in the ability to make predictions from data, purporting to improve decision-making. However, in cases where humans are still responsible for the final decision, biases in probabilistic reasoning can render even informative AI predictions detrimental to decision-making outcomes. Using a randomised experiment with loan underwriters, we show that the provision of coarsened AI signals at the right thresholds can improve overall decision-making in spite of information loss.
External Link(s)

Registration Citation

Citation
Hoong, Ruru (Juan Ru) and Bnaya Dreyfuss. 2024. "Coarsening Signals in Human-AI Interaction - Experiment 1." AEA RCT Registry. July 03. https://doi.org/10.1257/rct.13716-2.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Participants see different types of AI signals to aid their decision-making.
Intervention Start Date
2024-06-03
Intervention End Date
2024-07-31

Primary Outcomes

Primary Outcomes (end points)
The main dependent variable we will look at is decision accuracy (i.e. whether the decision to approve/deny the loan was ex-post correct).

Our main specification will use data from all rounds in the experiment. Though we will not be as well powered, we will later also perform a robustness check on just first round decisions.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We recruit 300 loan underwriters to make loan application decisions.
Experimental Design Details
Not available
Randomization Method
Randomisation done through Otree programme
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
300 individuals.
Sample size: planned number of observations
We will aim to recruit 300 subjects. We will filter out subjects in our analysis based on attention and time checks.
Sample size (or number of clusters) by treatment arms
300 in each arm. Each subject will see every treatment arm in randomised order.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Harvard University
IRB Approval Date
2024-04-12
IRB Approval Number
IRB24-0395
Analysis Plan