Perceptions of Artificial Intelligence in Recruitment

Last registered on May 30, 2022
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Pre-Trial

Trial Information

General Information

Title
Perceptions of Artificial Intelligence in Recruitment
RCT ID
AEARCTR-0008555
Initial registration date
January 11, 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
January 11, 2022, 9:16 AM EST

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

Last updated
May 30, 2022, 10:49 AM EDT

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

Locations

Country
United Kingdom of Great Britain and Northern Ireland
Region

Primary Investigator

Name
Paris Will
Affiliation
London School of Economics
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Other Primary Investigator(s)

PI Name
Dario Krpan
PI Affiliation
London School of Economics and Political Science
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PI Name
Grace Lordan
PI Affiliation
London School of Economics and Political Science
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PI Name
Teresa Almeida
PI Affiliation
London School of Economics and Political Science
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PI Name
Yolanda Blavo
PI Affiliation
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Additional Trial Information

Status
In development
Start date
2022-01-10
End date
2022-12-01
Keywords
Firms & Productivity, Gender
Additional Keywords
Artificial intelligence; hiring; fairness
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
As new technologies emerge, hiring is becoming increasingly automated. Organisations are beginning to utilise Artificial Intelligence for the purposes of making hiring decisions, but this adoption has been controversial. Opinions differ on the outcomes of using Artificial Intelligence , with some claiming it can reduce bias, and others claiming it will perpetuate human biases. In particular, assessing candidate perceptions towards the use of Artificial Intelligence in the hiring process has been an emerging area of research, as understanding how candidates feel towards the technology can have implications for the competitive war for talent. This study looks at the perceived fairness and intentions to seek litigation from candidates in a simulated hiring process. Using a vignette style experiment, participants read a hiring process and then state their opinions on it as well as answer questionnaires on individual differences. We assess 9 hiring process conditions with a 3x3 between-subjects design varying on hiring decision maker and stage of recruitment process.
External Link(s)

Registration Citation

Citation
Will, Paris et al. 2022. "Perceptions of Artificial Intelligence in Recruitment." AEA RCT Registry. May 30. https://doi.org/10.1257/rct.8555-1.3
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2022-01-10
Intervention End Date
2022-12-01

Primary Outcomes

Primary Outcomes (end points)
perceived fairness, litigation intention
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This study takes a 3x3 between-subjects design where subjects are randomly assigned to one of 9 conditions based on hiring decision maker and stage of recruitment process.
Experimental Design Details
The 9 experimental conditions are; hiring decision maker (1. human, 2. artificial intelligence, 3. human/artificial intelligence mix) x stage of recruitment process (1. CV, 2. psychometric, 3. interview). Gender of recruiter (female, male, gender neutral) and size of organization (medium or large) are also randomized.
Randomization Method
Randomization through Qualtrics survey. Settings have equal number of participants completing each condition.
Randomization Unit
individual participants
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
900 individuals
Sample size: planned number of observations
900 individuals
Sample size (or number of clusters) by treatment arms
100 individuals per condition
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
London School of Economics and Political Science
IRB Approval Date
2021-12-08
IRB Approval Number
50767
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