AI, Complexity, and Explanations

Last registered on January 09, 2026
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Pre-Trial

Trial Information

General Information

Title
AI, Complexity, and Explanations
RCT ID
AEARCTR-0017444
Initial registration date
December 11, 2025

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
January 09, 2026, 8:39 AM EST

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

Locations

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Primary Investigator

Name
Eva Hümmecke
Affiliation
RWI – Leibniz Institute for Economic Research
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Other Primary Investigator(s)

PI Name
Mark Andor
PI Affiliation
RWI – Leibniz Institute for Economic Research
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PI Name
Simon Damm
PI Affiliation
Ruhr University Bochum
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PI Name
Christoph Feldhaus
PI Affiliation
Ruhr University Bochum
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PI Name
Asja Fischer
PI Affiliation
Ruhr University Bochum
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PI Name
Leon Landes
PI Affiliation
Ruhr University Bochum
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PI Name
Andreas Löschel
PI Affiliation
Ruhr University Bochum
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Additional Trial Information

Status
In development
Start date
2025-12-12
End date
2030-12-31
Keywords
Behavior
Additional Keywords
Artificial Intelligence, Algorithm Aversion, Task Complexity, XAI, Interpretability, Explanation
JEL code(s)
D83, D81, D91, O33
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
As artificial intelligence (AI) systems increasingly make or influence decisions on behalf of individuals, understanding the conditions under which people accept and thus place trust in AI generated decisions becomes critically important. We conduct an incentivized experiment to causally estimate how task complexity and AI-generated explanations affect acceptance of AI interventions relative to human ones. Participants face lottery choices presented in a simple or a compound format. The lotteries are constructed to have different variances and expected values. An intervening agent, either a human (another participant) or an AI system, then selects the lottery that is played. After observing this intervened choice, participants decide whether to overrule it and choose themselves. We then elicit their willingness to accept compensation for keeping or overruling the agent’s choice. We further test whether explanations provided by the AI system influence acceptance of AI generated decisions. The design allows us to isolate the causal effects of complexity and explanations on acceptance for AI interventions relative to human decision makers.
External Link(s)

Registration Citation

Citation
Andor, Mark et al. 2026. "AI, Complexity, and Explanations." AEA RCT Registry. January 09. https://doi.org/10.1257/rct.17444-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-12-12
Intervention End Date
2026-01-15

Primary Outcomes

Primary Outcomes (end points)
Overruling choice, willingness to accept/overrule intervened decision
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Participants are randomly assigned to one of eight experimental groups in a 2×4 orthogonal design. The first dimension of the design varies the complexity of the choice participants face (low vs. high complexity). The second dimension varies the entity that intervenes in the decision (human intervention, AI intervention, explainable AI intervention (ex ante) and explainable AI intervention (ex post)). We then measure whether the humans or the AI’s decision is overruled. We also measure the WTA to keep the intervened decision.
Experimental Design Details
Not available
Randomization Method
Randomization by the survey institute commissioned to conduct the survey
Randomization Unit
Individuals (respondents)
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
9,000 respondents (3,000 per country: US, Spain and Germany)
Sample size: planned number of observations
9,000 respondents (3,000 per country: US, Spain and Germany)
Sample size (or number of clusters) by treatment arms
Before the experiment, 600 participants act as the decider and are not assigned to the experimental groups.

8,400 participants are randomly assigned to one of eight experimental groups in a 2×4 orthogonal design:
first dimension: 4,200 simple lottery, 4,200 complex lottery
second dimension: 1,260 intervention another participant, 1,260 intervention AI + explanation ex ante, 420 intervention AI + explanation ex post, 1,260 intervention AI (control)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
German Association for Experimental Economic Research e.V.
IRB Approval Date
2025-11-24
IRB Approval Number
m9byWZg1
Analysis Plan

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