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Field
Trial Status
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Before
completed
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After
in_development
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Field
Trial Start Date
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Before
December 19, 2022
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After
April 24, 2023
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Field
Trial End Date
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Before
December 21, 2022
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After
May 05, 2023
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Field
Last Published
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Before
March 01, 2023 07:22 AM
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After
April 17, 2023 08:50 AM
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Field
Intervention Start Date
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Before
December 19, 2022
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After
April 24, 2023
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Field
Intervention End Date
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Before
December 21, 2022
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After
May 05, 2023
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Field
Experimental Design (Public)
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Before
The basic procedure of the experiment is the following: In stage 1, before informing the participants
about subsequent stages, we elicit attributes about participants via a questionnaire. These attributes
serve as input features for the AI-based risk attitude prediction. In stage 2, after the explanation of the
experiment, we present the participants the full list of the 200 possible binary lotteries with censored
payoffs. The lotteries differ in terms of their risk level as well as the goodness of the potential payoffs
(see subsection ‘Details on stage 1’ for more information). After the presentation of the full lottery list,
we introduce the AI system to the participants. The AI system is trained to predict participants’ risk
attitudes based on the information provided in the initial questionnaire. Using this prediction, the AI
system filters the five most suitable lotteries from the full list of 200 lotteries for each individual
participant. In stage 3, we perform the main treatment manipulation. We allow treatment participants
to perform decentralized feature selection. Participants in the baseline group do not get this
opportunity; in their cases, the AI system processes all information elicited in the initial questionnaire.
Following that, we ask all participants to state their willingness to pay (WTP) for leveraging the AI
system using the Becker-DeGroot-Marschak (BDM) method. In stage 4, participants make their lottery decision. Participants whose WTP was high enough for
receiving the AI support may inspect both the AI-based lottery preselection and the full list of 200
lotteries. Participants whose WTP did not reach the critical BDM-threshold do not receive the
AI-based preselection. In stage 5 we elicit the participants’ perception of the overall AI system, the
perception of the AI system’s prediction accuracy and the participants' revealed risk attitude.
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After
The basic procedure of the experiment is the following: In stage 1, before informing the participants
about subsequent stages, we elicit attributes about participants via a questionnaire. These attributes
serve as input features for the AI-based risk attitude prediction. In stage 2, after the explanation of the
experiment, we present the participants the full list of the 200 possible binary lotteries with censored
payoffs. The lotteries differ in terms of their risk level as well as the goodness of the potential payoffs
(see subsection ‘Details on stage 1’ in the attached file for more information). After the presentation of the full lottery list,
we introduce the AI system to the participants. The AI system is trained to predict participants’ risk
attitudes based on the information provided in the initial questionnaire. Using this prediction, the AI
system recommends the five most suitable lotteries from the full list of 200 lotteries to each individual
participant. In stage 3, we perform the treatment manipulation. In a within-subjects design, we present
participants two different scenarios: One scenario where participants may perform decentralized
feature selection to influence the AI system (treatment condition), and another scenario without
decentralized feature selection, i.e. the AI system uses all participant attributes elicited in the initial
questionnaire (baseline condition). In each scenario, we ask participants to state their willingness to
pay (WTP) for leveraging the respective AI system. In stage 4, participants make their lottery decision.
Participants whose WTP was high enough for receiving the AI support may inspect both the AI-based
lottery recommendations and the full list of 200 lotteries. Participants whose WTP did not reach the
critical BDM-threshold do not receive the AI-based recommendations. In stage 5 we elicit additional
secondary measures as well as participants’ revealed risk attitudes.
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Field
Planned Number of Observations
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Before
300
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After
400
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Field
Sample size (or number of clusters) by treatment arms
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Before
150 participants per group (treatment vs. baseline).
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After
200 participants per group (treatment vs. baseline).
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Field
Additional Keyword(s)
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Before
Artificial Intelligence
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After
Artificial Intelligence, Human-Computer-Interaction
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Field
Intervention (Hidden)
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Before
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After
The complete analysis plan is attached.
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Field
Secondary Outcomes (End Points)
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Before
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After
(1) Transparency
(2) Power
(3) Privacy intrusion
(4) Curiosity in ML prediction
(5) Perceived accuracy of the AI system
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Field
Pi as first author
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Before
No
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After
Yes
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