Efficiency vs. Fairness tradeoff in personnel selection: Study of human perceptions

Last registered on June 27, 2022

Pre-Trial

Trial Information

General Information

Title
Efficiency vs. Fairness tradeoff in personnel selection: Study of human perceptions
RCT ID
AEARCTR-0009663
Initial registration date
June 27, 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
June 27, 2022, 4:49 PM EDT

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

Locations

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Primary Investigator

Affiliation
University of Zurich

Other Primary Investigator(s)

PI Affiliation
University of St.Gallen

Additional Trial Information

Status
On going
Start date
2022-06-10
End date
2022-08-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study explores human perceptions of algorithms in HR context. This study follows our previous experiments on people's perception of efficiency vs. fairness tradeoff in personnel selection (AEARCTR-0008804). Here, we modify the scenario and focus on female dominated occupation to investigate how the degree of efficiency vs. fairness tradeoff affects people' choices of the algorithms and their fairness perceptions.
External Link(s)

Registration Citation

Citation
Kandul, Serhiy and Ulrich Leicht-Deobald. 2022. "Efficiency vs. Fairness tradeoff in personnel selection: Study of human perceptions." AEA RCT Registry. June 27. https://doi.org/10.1257/rct.9663
Experimental Details

Interventions

Intervention(s)
We add a scenario with female dominated occupation and manipulate fairness metric (equality of opportunity vs. statistical partiy) and degree of efficiency vs. fairness tradeoff.
The intervention therefore closely follows the one described in prevoius trials AEARCTR-0008804
Intervention Start Date
2022-06-30
Intervention End Date
2022-07-10

Primary Outcomes

Primary Outcomes (end points)
The frequency of choices of a fair algorithm; fairness perceptions
Primary Outcomes (explanation)
fairness perceptions will be derived as average score from the respective scales

Secondary Outcomes

Secondary Outcomes (end points)
participants' reasoning behind their choices; participants' beliefs about existing inqualities on labor market.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We employ 2 (fairness metric) by 2 (female or male advantage) design. The degree of the tradeoff between efficiency and fairness of the selection algorithm is a within-subject manipulation.
Experimental Design Details
Not available
Randomization Method
Randomization by Qualtrics.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We plan to recruit 280 participants (60 per each experimental condition)
Sample size: planned number of observations
280
Sample size (or number of clusters) by treatment arms
60
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number