AI Empowerment or AI Replacement

Last registered on October 17, 2023
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Pre-Trial

Trial Information

General Information

Title
AI Empowerment or AI Replacement
RCT ID
AEARCTR-0012196
Initial registration date
October 13, 2023

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
October 17, 2023, 1:38 PM EDT

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

Locations

Country
China
Region
Wuhan

Primary Investigator

Name
Lian XUE
Affiliation
Wuhan University
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Other Primary Investigator(s)

PI Name
Lijia Wei
PI Affiliation
Wuhan University
Contact Investigator
PI Name
Lu Bai
PI Affiliation
Wuhan University
Contact Investigator
PI Name
Lijia Wei
PI Affiliation
Wuhan University
Contact Investigator

Additional Trial Information

Status
On going
Start date
2023-10-01
End date
2023-11-20
Keywords
Behavior, Lab
Additional Keywords
Public goods game; Artificial Intelligence
JEL code(s)
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
In our earlier experiment, centered around social and AI scoring, we painted an optimistic picture of AI's introduction. Not only did we find that AI scoring systems enhance human cooperation, but individuals also demonstrated a growing acceptance of these systems, particularly as they gained more experience or encountered deteriorating cooperative environments.
However, concerns arise regarding the potential for excessive automation resulting from AI implementation. The replacement of humans by AI may lead to undesirable social consequences such as job loss, stagnant wages, and increased inequality. To address these concerns, we introduced a new treatment aimed at discussing the potential of AI restatement – a type of AI development that augments workers' productivity and creates new tasks for labor-intensive roles.
Our study delved into the dual functions of AI – replacement and reinstatement – and employed experimental methods to investigate individual preferences and their associated outcomes concerning these two AI applications.
External Link(s)

Registration Citation

Citation
Bai, Lu et al. 2023. "AI Empowerment or AI Replacement." AEA RCT Registry. October 17. https://doi.org/10.1257/rct.12196-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Our specific objective is to introduce a novel intervention, granting participants the autonomy to determine the dynamics of their relationship with the decision-making process, which involves the integration of their own choices with AI assistance.

In conjunction with the existing AI-score-punish and Social-Score-punish treatments, which incorporate punitive measures, we provide participants with the opportunity to decide whether they wish to receive guidance from the AI while retaining ultimate decision-making authority. We refer to this distinct treatment as the AI-assistant (AI-assist) treatment.
Intervention Start Date
2023-10-20
Intervention End Date
2023-11-18

Primary Outcomes

Primary Outcomes (end points)
We will examine two main outcome variables: 1. Preference. 2. Contributions.
Primary Outcomes (explanation)
Preference: We aim to discern the proportion of participants who favor AI-assistance and those who lean towards AI-delegation.

Contributions: In addition, we will categorize the population into four distinct groups using a 2 by 2 classification, which takes into account whether participants prefer AI-assistance and whether they are part of a group where the eventual outcome aligns with AI-assistance. We will then analyze participants' contributions within these four outcome categories.

It is important to note that the utilization of AI-assistance significantly impacts participants' inclinations toward cooperative behavior, and the specific path of assistance chosen also has varying effects on their behavior.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our experimental design involves a public goods game with participants grouped into foursomes, playing 20 rounds. Like in the "Digital Punishment" setup, two participants get 20 tokens, while the other two get 40 tokens initially. We set a marginal per capita return (MPCR) at 1.6. We depart from standard practices by introducing rematching every 10 rounds, closely related to our treatment groups. In this new treatment, instead of voting for whether or not to incorporate the AI or Social score system in their group task, participants choose between two ways AI could influence their group decision-making - one in which AI scores everyone and implements corresponding punitive measures, the other in which AI makes scoring suggestions and participants themselves make final judgment and decisions.
Experimental Design Details
Randomization Method
The process of randomization was meticulously conducted within the confines of the laboratory using sophisticated computer algorithms. Our randomization will be done by computers.
Randomization Unit
The unit of observation can be defined at varying levels of granularity, encompassing the individual-period level, group-period level, or group level, contingent upon the specific hypotheses being tested.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
We aim to run 4-6 sessions for this treatment.
Sample size: planned number of observations
Each session has 16-20 participants.
Sample size (or number of clusters) by treatment arms
We aimed to run a minimum of 80 participants in this treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Center of Behavior and Economic Reasearch
IRB Approval Date
2023-09-20
IRB Approval Number
IRB202301088
Analysis Plan

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