Trust in hiring algorithms: causal effect of beliefs about humans’ discrimination for workers, and self-confidence for managers

Last registered on May 16, 2022

Pre-Trial

Trial Information

General Information

Title
Trust in hiring algorithms: causal effect of beliefs about humans’ discrimination for workers, and self-confidence for managers
RCT ID
AEARCTR-0009068
Initial registration date
March 17, 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
March 21, 2022, 1:23 PM EDT

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

Last updated
May 16, 2022, 9:46 AM EDT

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

Locations

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Primary Investigator

Affiliation
Paris Dauphine University

Other Primary Investigator(s)

PI Affiliation
University of Lausanne & WZB Berlin
PI Affiliation
WZB Berlin, TU Berlin

Additional Trial Information

Status
In development
Start date
2022-03-21
End date
2022-06-15
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We run an online experiment to study ways to overcome algorithm aversion. We use a 2*2 design. Participants are either in the role of workers or of managers. They will furthermore either be assigned to the baseline or the treatment. Workers perform three real-effort tasks: task 1, task 2 and the job task. They must choose whether they prefer hiring decisions between themsef and another worker to be made by a participant in the role of a manager or an algorithm. They will get paid if they are hired in one random pair of workers they belong to. They know that the manager will get paid if they hire the best worker at the job task out of a random pair of workers they have made a hiring decision for. The managers and the algorithm will base their hiring decision on the genders of the workers and their task 1 and task 2 perfromances. In the workers' treatment, before deciding between the manager and the algorithm, workers are informed of the proportion of men and women who were chosen to be hired by the managers in a managers' session. Managers must make 20 hiring decisions between pairs of workers. They can see the gender of the workers and their task 1 and task 2 performances. We elicit for how many of the 20 decisions they believe they have hired the best worker of the pair. They must then decide whether they want to delegate the hiring decisions to the algorithm. If they do, their payoff will depend on the algorithm's hiring decision, not theirs. In the managers' treatment, managers will get a feedback about their over/under or well-calibrated confidence before the decision whether to delegate the hiring decisions to the algorithm
External Link(s)

Registration Citation

Citation
Dargnies, Marie-Pierre, Rustamdjan Hakimov and Dorothea Kübler. 2022. "Trust in hiring algorithms: causal effect of beliefs about humans’ discrimination for workers, and self-confidence for managers." AEA RCT Registry. May 16. https://doi.org/10.1257/rct.9068
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2022-03-21
Intervention End Date
2022-06-15

Primary Outcomes

Primary Outcomes (end points)
For both workers and managers, we are interested in whether the treatments decrease algorithm aversion.
For workers, a decreased algorithm aversion would mean a higher proportion of workers would choose that the hiring decisions are made by the algorithm in the workers' treatments than in the workers' baseline.
For managers, a decreased algorithm aversion would mean a higher proportion of managers would choose to delegate the hiring decisions to the algorithm in the managers' treatment than in the managers' baseline.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
We are interested in finding additional evidence of algorithm aversion and investigating whether it depends on the confidence of workers in their relative performance and the confidence of managers in the quality of their hiring decisions.

We are also interested in potentially different effects of the treatment depending on the gender of the participant, their belief about the discriminative behavior of the algorithm compared to that of the manager and , the confidence of workers in their relative performance, and the confidence of managers in the quality of their hiring decisions.

Finally, we are interested in whether workers' beliefs about how discriminatory the algorithm is changes with the treatments.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We run an online experiment to study ways to overcome algorithm aversion. Participants are either in the role of workers or of managers. Workers will either be assigned to the baseline, treatment 1, treatment 2 or treatment 3. Managers will either be assigned to the baseline, treatment 1 or treatment 2. Workers perform three real-effort tasks: task 1, task 2 and the job task. They must choose whether they prefer hiring decisions between themself and another worker to be made by a participant in the role of a manager or an algorithm. They will get paid if they are hired in one random pair of workers they belong to. They know that the manager will get paid if they hire the best worker at the job task out of a random pair of workers they have made a hiring decision for. In the workers baseline and treatments 1 and 3, the managers and the algorithm will base their hiring decision on the genders of the workers and their task 1 and task 2 performances. In the workers' treatment 1, before deciding between the manager and the algorithm, workers are informed of the proportion of men and women who were chosen to be hired by the managers in a managers' session. In the workers' treatment 2, the algorithm will base its hiring decisions on the task 1 and task 2 performances of the workers but not on their gender. In the workers' treatment 3, details will be given about how the algorithm works to decide which workers to hire. Managers must always make 20 hiring decisions between pairs of workers. They can see the gender of the workers and their task 1 and task 2 performances. We elicit for how many of the 20 decisions they believe they have hired the best worker of the pair. They must then decide whether they want to delegate the hiring decisions to the algorithm. If they do, their payoff will depend on the algorithm's hiring decision, not theirs. In the managers' treatment 1, managers will get a feedback about their over/under or well-calibrated confidence before the decision whether to delegate the hiring decisions to the algorithm. In the managers' treatment 2, details are given about how the algorithm works before managers have to decide whether to delegate the hiring decisions to the algorithm.
Experimental Design Details
Not available
Randomization Method
The participants will select into one of the treatments (Baseline workers, Treatment 1 workers, Treatment 2 workers, Treatment 3 workers, Baseline managers, Treatment 1 managers, Treatment 2 managers) randomly.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1750 individuals
Sample size: planned number of observations
1750 individuals
Sample size (or number of clusters) by treatment arms
250 in each of the treatments : Baseline workers, Treatment 1 workers, Treatment 2 workers, Treatment 3 workers, Baseline managers, Treatment 1 managers, Treatment 2 managers.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
TRIAL University Lausanne HEC IRB Board
IRB Approval Date
2022-03-09
IRB Approval Number
N/A
Analysis Plan

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