AI perception in personnel selection: Stakeholder perspective

Last registered on April 13, 2023
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Pre-Trial

Trial Information

General Information

Title
AI perception in personnel selection: Stakeholder perspective
RCT ID
AEARCTR-0011208
Initial registration date
April 07, 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
April 13, 2023, 4:04 PM EDT

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

Last updated
April 13, 2023, 4:15 PM EDT

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

Locations

Country
Switzerland
Region

Primary Investigator

Name
Serhiy Kandul
Affiliation
University of Zurich
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Ulrich Leicht-Deobald
PI Affiliation

Additional Trial Information

Status
On going
Start date
2023-04-05
End date
2023-05-31
Keywords
Behavior
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
AI-based personell selection tools are efficient. However, they can generate disparities in selection rates across groups of applicants and therefore violate fairness requirements. In this project we investigate how people perceive this efficiency-fairnes tradeoffs. After having observed people's general fairness evaluation and pferences over efficient and fair algorithms, in this study we are interested in how the stakeholder perspective affects the AI perceptions in HR context. For this purpose, we compare the tradeoff perceptions by the UK managers and students in the role of job applicants. Based on the prominent fairness theory in organizational justice, we expect that managers prefer more efficient algorithms, while students favour les efficient algorithms with lower disparity rates.
External Link(s)

Registration Citation

Citation
Kandul, Serhiy and Ulrich Leicht-Deobald. 2023. "AI perception in personnel selection: Stakeholder perspective." AEA RCT Registry. April 13. https://doi.org/10.1257/rct.11208-1.1
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We manipulate stakeholder perspective and see how this affects people's preferences over efficient and fair selection algorithms
Intervention Start Date
2023-04-08
Intervention End Date
2023-04-30

Primary Outcomes

Primary Outcomes (end points)
Fairness perceptions; preferences (choices) over algorithms
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We present participants with a series of selection algorithms. The algorithms are defined by two major features: efficiency, measured as the quality of the selection, and faireness, measured by the degree of disparity in selection rates across gender. There is always efficiency-fairnes tradeoffs. We manipulate the stakeholder perspective (between-subjects): the managers take the role of HR managers in the company, while students play as job applicants and the degree of the efficiency-fairness tradeoffs (within-subject). We then compare the fairness perceptions and choices over algorithms of managers and students.
Experimental Design Details
Randomization Method
randomization is done by software Qualtrics
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
480
Sample size: planned number of observations
240*2=480
Sample size (or number of clusters) by treatment arms
280
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 Approval Date
IRB Approval Number
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