Training Policymakers in AI

Last registered on October 25, 2021

Pre-Trial

Trial Information

General Information

Title
Training Policymakers in AI
RCT ID
AEARCTR-0008431
Initial registration date
October 23, 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
October 25, 2021, 2:44 PM EDT

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

Locations

Region

Primary Investigator

Affiliation
New Economic School Moscow

Other Primary Investigator(s)

PI Affiliation
Lahore School of Economics
PI Affiliation
Toulouse School of Economics

Additional Trial Information

Status
In development
Start date
2021-11-09
End date
2021-11-10
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In this experiment with senior deputy ministers of Pakistan, we randomize training policymakers in an “AI for Policy” workshop. The first treatment arm randomly receives a training focused on utility or benefits of AI for policymaking. The second treatment arm receives training focused on potential harms or dangers of AI. A control group received no training. Our design will elicit demand for both trainings and study the mediating channel experimentally using a book lottery in a simplified Becker-Degroot-Marschak mechanism. Using this lottery that randomly implements an earlier action to causally isolate the effect of book choice allows us to experimentally disentangle the effect of training from randomly assigned book conditional on the training’s demand. The rich administrative dataset and close collaboration with the Government of Pakistan will give us access to suit of measurements including actual policy outcomes.
External Link(s)

Registration Citation

Citation
Chen, Daniel , Sultan Mehmood and Shaheen Naseer. 2021. "Training Policymakers in AI." AEA RCT Registry. October 25. https://doi.org/10.1257/rct.8431-1.0
Experimental Details

Interventions

Intervention(s)
We randomize training policymakers in an “AI for Policy” workshop.

Treatment 1: The first treatment arm randomly receives a training focused on utility or benefits of AI for policymaking.

Treatment 2: The second treatment arm receives training focused on potential harms or dangers of AI.

A control group receives no training.

The design also studies a mediating channel experimentally using a book lottery in a simplified Becker-Degroot-Marschak mechanism. Using this lottery that randomly implements an earlier action to causally isolate the effect of book choice allows us to experimentally disentangle the effect of training from randomly assigned book conditional on the training’s demand.
Intervention (Hidden)
Intervention Start Date
2021-11-09
Intervention End Date
2021-11-10

Primary Outcomes

Primary Outcomes (end points)
self-reported benefits of AI for policy, actual policy decisions
Primary Outcomes (explanation)
self-reported benefits of AI for policy will be perceived importance of AI for policy using surveys. Actual policy decisions will be obtained from administrative from government of Pakistan.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design

Treatment 1: The first treatment arm randomly receives a training focused on utility or benefits of AI for policymaking.

Treatment 2: The second treatment arm receives training focused on potential harms or dangers of AI.

300 deputy ministers are randomly assigned in treatment 1, 2 or control group.
Experimental Design Details
Randomization Method
randomization done in office by a computer at the individual level using Stata.
Randomization Unit
individual.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
300 at the individual level
Sample size: planned number of observations
300
Sample size (or number of clusters) by treatment arms
300 with 100 clusters per treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
The IRB application granted tentative approval and awaiting final approval.
IRB Approval Date
2021-10-18
IRB Approval Number
N/A

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