Effect of A.I. Evaluators on Creative Output

Last registered on November 19, 2024

Pre-Trial

Trial Information

General Information

Title
Effect of A.I. Evaluators on Creative Output
RCT ID
AEARCTR-0014860
Initial registration date
November 18, 2024

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
November 19, 2024, 4:38 PM EST

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

Locations

Region

Primary Investigator

Affiliation
University of Arizona

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2024-11-18
End date
2024-12-02
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Previous research suggests that quantity-based incentives can be just as effective as quality-based incentives in driving creative outcomes. A potentially non-trivial cost to quantity-based incentives, though, is that it increases the number of items that managers need to evaluate. Artificial intelligence (A.I.) programs provide a way for managers to evaluate creative content quickly, allowing them to assess the quality of a large amount of content in a relatively short amount of time. However, the use of A.I. to evaluate the quality of creative content could discourage workers from developing and sharing their innovative ideas if they are uncertain as to how A.I. will evaluate their content, or if they are meaningfully averse towards algorithmic evaluations of their creative content. We conduct an experiment to see how participation in a creative idea-sharing contest is impacted based on the types of evaluators that are used to evaluate the submissions—A.I. evaluators versus human evaluators—and based on whether quantity-based incentives or quality-based incentives are used.
External Link(s)

Registration Citation

Citation
Sandvik, Jason. 2024. "Effect of A.I. Evaluators on Creative Output." AEA RCT Registry. November 19. https://doi.org/10.1257/rct.14860-1.0
Experimental Details

Interventions

Intervention(s)
Participants will begin by answering basic demographic questions in a Qualtrics survey. They will then answer questions about their feelings towards human evaluators and A.I. evaluators, and they'll provide details about their experiences in and knowledge of New York City.

Participants will be asked whether or not they want to create and submit short promotions ("promos", of 300 characters or less) to get people to visit New York City. They will either be told that their promos will be evaluated by "several people with marketing experience" or "an artificial intelligence (A.I.) program that is specifically designed for use in marketing." Each promo will be evaluated on a 0-100 scale based on how likely it is that someone will click on the promo to learn more. To incentivize participation, they will either be told they will "receive $0.50 (50 cents) for each promo they submit, so long as the promo is evaluated with a score above 33. If it is evaluated with a score of 33 or lower, no payment will be provided for that particular promo" or "receive $50 for each promo you submit that is among the 10 highest-scoring promos in the entire contest. Approximately one out of every ten participants is expected to win $50." In both cases, they will be told that they can submit up to 10 different promos to increase their potential payout.

After learning all this information, they'll be asked if they want to enter the contest. For those that say no, the survey will end and they'll receive the pre-contracted payment amount. For those that say yes, they'll have approximately one hour to submit their promos. Once they finish, they'll receive the pre-contracted payment amount and then, after the promos are evaluated, they'll receive additional payments given the parameters of their contest and the quality of their submitted promos.
Intervention (Hidden)
Intervention Start Date
2024-11-18
Intervention End Date
2024-12-02

Primary Outcomes

Primary Outcomes (end points)
The decision to enter the contest (yes/no response to the question asking if they want to participate). The number of promos submitted in the contest for those who enter.
Primary Outcomes (explanation)
The decision to enter will be based on their yes/no response to the question that explicitly asks if they want to enter the contest (after having learned all the parameter details of their specific contest). The number of promos will be based on the free-response entries they provide, with a limit of ten promos and with the caveat that they cannot submit the exact same promos multiple times.

Secondary Outcomes

Secondary Outcomes (end points)
Average quality of each promo, as evaluated on the 0-100 scale by (i) the A.I evaluator, (ii) the human evaluators, and (iii) all evaluators. We will also consider how participation (primary outcomes) and promo quality (secondary outcomes) vary based on the demographic characteristics of the participants (e.g., whether they from a demographic group that is traditionally underrepresented in creative endeavors or whether they exhibit ex ante high levels of algorithm aversion).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Participants will begin by answering basic demographic questions in a Qualtrics survey. They will then answer questions about their feelings towards human evaluators and A.I. evaluators, and they'll provide details about their experiences in and knowledge of New York City.

Participants will be asked whether or not they want to create and submit short promotions ("promos", of 300 characters or less) to get people to visit New York City. They will either be told that their promos will be evaluated by "several people with marketing experience" (human evaluator) or "an artificial intelligence (A.I.) program that is specifically designed for use in marketing" (A.I. evaluator). Each promo will be evaluated on a 0-100 scale based on how likely it is that someone will click on the promo to learn more. To incentivize participation, they will either be told they will "receive $0.50 (50 cents) for each promo they submit, so long as the promo is evaluated with a score above 33. If it is evaluated with a score of 33 or lower, no payment will be provided for that particular promo" (quantity-based incentives) or "receive $50 for each promo you submit that is among the 10 highest-scoring promos in the entire contest. Approximately one out of every ten participants is expected to win $50" (quality-based incentives). In both cases, they will be told that they can submit up to 10 different promos to increase their potential payout.

After learning all this information, they'll be asked if they want to enter the contest. For those that say no, the survey will end and they'll receive the pre-contracted payment amount. For those that say yes, they'll have approximately one hour to submit their promos. Once they finish, they'll receive the pre-contracted payment amount and then, after the promos are evaluated, they'll receive additional payments given the parameters of their contest and the quality of their submitted promos.

Randomization will occur via the block-randomization feature in Qualtrics. 25% of participants will be randomized into the A.I. evaluator and quantity-based incentives treatment; 25% of participants will be randomized into the A.I. evaluator and quality-based incentives treatment; 25% of participants will be randomized into the human evaluator and quantity-based incentives treatment; and 25% of participants will be randomized into the human evaluator and quality-based incentives treatment.

The only differences in content across the four treatment cells will be the language about the types of evaluators who will evaluate the promos and the types of incentives in place for participating in the contest.
Experimental Design Details
Randomization Method
Randomization via the Qualtrics block-randomization feature.
Randomization Unit
Individual labor market participant
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1,000 labor market participants
Sample size: planned number of observations
1,000 labor market participants
Sample size (or number of clusters) by treatment arms
250 labor market participants in A.I. evaluator x quantity-based incentives
250 labor market participants in A.I. evaluator x quality-based incentives
250 labor market participants in human evaluator x quantity-based incentives
250 labor market participants in human evaluator x quality-based incentives
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Texas State University IRB
IRB Approval Date
2024-09-13
IRB Approval Number
9772

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