Understanding Attitudes toward Autonomous Vehicle in Tort Liability: Randomized Experiments

Last registered on December 09, 2025
View Trial History

Pre-Trial

Trial Information

General Information

Title
Understanding Attitudes toward Autonomous Vehicle in Tort Liability: Randomized Experiments
RCT ID
AEARCTR-0017422
Initial registration date
December 08, 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
December 09, 2025, 8:17 AM EST

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

Name
Jeong Yeol Kim
Affiliation
KDI School of Public Policy and Management
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Syngjoo Choi
PI Affiliation
Seoul National University
Contact Investigator
PI Name
Hai Jin Park
PI Affiliation
Hanyang University
Contact Investigator
PI Name
Seungmi You
PI Affiliation
Seoul National University
Contact Investigator
PI Name
Yeojoon Cho
PI Affiliation
Seoul National University
Contact Investigator

Additional Trial Information

Status
On going
Start date
2025-12-01
End date
2026-03-31
Keywords
Behavior
Additional Keywords
Autonomous vehicles, Tort law, Comparative negligence, Responsibility allocation, Survey experiment, Information treatment, Behavioral law and economics, Non-pecuniary damages
JEL code(s)
K13, K41, C90, D91, O33
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Autonomous vehicles present fundamental challenges to tort liability systems designed for human drivers. This study examines whether laypeople exhibit systematic bias in fault attribution and damage awards when defendants operate autonomous vehicles rather than conventional vehicles, and whether information about tort law's preventive function moderates these judgments.

We conduct a pre-registered randomized experiment with 4,200 participants using a 2×2 factorial design. The first factor varies information content: participants receive either general civil litigation information or information emphasizing tort law's preventive function in promoting safety investments. The second factor varies engagement: half of participants answer support questions about the preventive function of tort law while half do not. Following treatment, all participants complete six vignette-based liability scenarios as mock judges in traffic accident cases. Each scenario systematically manipulates defendant vehicle type (human-driven vs. autonomous), accident preventability, vehicle performance characteristics, and plaintiff fault levels.

Our design addresses two research questions. First, we estimate whether autonomous vehicle defendants face higher fault attributions or damage awards compared to human drivers in otherwise identical accidents, testing for autonomous vehicle aversion in liability judgments. Second, we test whether information emphasizing tort law's deterrence function reduces or amplifies any observed bias, examining whether framing tort law as a safety incentive mechanism rather than purely compensatory affects liability judgments differently for autonomous versus human defendants.

The vignette scenarios present realistic traffic accidents involving either two vehicles (intersection and highway settings) or vehicle-pedestrian collisions. We manipulate defendant vehicle technology (human-driven vs. fully autonomous), accident preventability (whether the defendant vehicle could have avoided the accident), autonomous vehicle performance capabilities (superior vs. equivalent to human drivers), and plaintiff baseline fault (equal to, lower than, or much lower than the defendant’s fault). Each participant evaluates six scenarios selected through constrained randomization to ensure exposure to different vehicle types while maintaining statistical power.

Primary outcomes include fault allocation percentages (comparative negligence determination) and solatium amounts (non-pecuniary damage awards). We measure both point estimates and participants' reported confidence in their judgments. This confidence measure enables analysis of decision certainty, which may differ between autonomous and human defendant cases even when average judgments appear similar.

We stratify randomization by age, gender, and metropolitan residence to enable analysis of heterogeneous treatment effects. These dimensions are policy-relevant because public acceptance of autonomous vehicles may vary systematically across demographic groups, and tort liability judgments by actual judges or juries may reflect similar heterogeneity. Our sample includes targeted recruitment from regions with ongoing autonomous vehicle development initiatives.

The experimental design enables causal identification of information treatment effects on liability judgments while the within-subject vignette variation permits estimation of autonomous vehicle bias through difference-in-differences analysis. By varying both information framing and engagement depth, we test whether passive information exposure suffices or whether active processing through support questions is necessary to shift liability judgments.

This study contributes to understanding how liability systems may need to adapt as autonomous vehicles become prevalent. If we observe systematic bias against autonomous vehicles, this suggests that current liability frameworks may inadvertently create excessive deterrence for autonomous vehicle deployment even when these vehicles demonstrably improve safety. Conversely, if information about tort law's preventive function reduces bias, this suggests that public education about liability system objectives could facilitate autonomous vehicle adoption while maintaining appropriate safety incentives.
External Link(s)

Registration Citation

Citation
Cho, Yeojoon et al. 2025. "Understanding Attitudes toward Autonomous Vehicle in Tort Liability: Randomized Experiments." AEA RCT Registry. December 09. https://doi.org/10.1257/rct.17422-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
This pre-registered study consists of a 2×2 factorial information experiment followed by a common vignette-based liability judgment task. In the first stage, participants are randomly assigned to watch either (i) a video providing general information about civil litigation procedures or (ii) a video emphasizing the preventive function of tort law. Within each information condition, they are further divided into (a) a group that proceeds directly to the next stage after watching the video and (b) a group that answers brief questions about the preventive function of tort law before moving on. In the second-stage vignette experiment, all participants act as mock judges. Traffic accident cases are presented as vignettes in 30 distinct scenarios that systematically vary whether the defendant operates a human-driven vehicle or an autonomous vehicle, whether the autonomous vehicle is equipped with state-of-the-art safety technology for accident prevention, whether its prediction-and-avoidance capabilities are superior to or equivalent to those of human drivers, and the plaintiff’s baseline level of fault (equal to, lower than, or much lower than the defendant’s fault). The 30 scenarios are categorized into five groups; for each participant, one scenario is randomly drawn from each group, and one additional scenario is randomly selected from the full set, so that each participant evaluates a total of six scenarios. For every scenario, participants allocate comparative fault between plaintiff and defendant, determine an amount for non-pecuniary damages, and report their confidence in each of these judgments.
Intervention Start Date
2025-12-01
Intervention End Date
2026-01-31

Primary Outcomes

Primary Outcomes (end points)
Defendant Responsibility Allocation

For each of the six vignette scenarios, we measure the percentage of responsibility allocated to the defendant using a continuous slider scale (0-100%), where 0% indicates the defendant bears no responsibility and 100% indicates the defendant bears full responsibility for the accident. This responsilibility ratio allocation determination directly measures whether participants assign different levels of responsibility to autonomous versus human-driven defendants.

Pain and Suffering Damages

For each vignette scenario, we measure the monetary award for non-pecuniary damages that participants determine the defendant must pay to the plaintiff, entered as a continuous amount in units of 10,000 Korean won (approximately $7.50 USD). Scenarios specify that economic damages (medical expenses, lost wages) total 30 million won, and participants determine appropriate additional compensation for pain, suffering, and emotional distress.
Primary Outcomes (explanation)
These outcomes directly test our core hypotheses about autonomous vehicle aversion in liability judgments. Responsibility allocation measures the fundamental determination in comparative negligence systems of how responsibility divides between parties. If participants systematically assign higher responsibility percentages to autonomous defendants than to human defendants in otherwise identical scenarios, this indicates bias that could create excessive deterrence for autonomous vehicle deployment.

Pain and suffering awards capture an independent dimension of liability that may reflect different psychological processes. Even if responsibility allocation appears neutral, differential damage awards could indicate that participants perceive harms differently when caused by autonomous vehicles, possibly due to violations of anthropomorphic expectations, reduced empathy for non-human defendants, or different intuitions about appropriate deterrence for algorithmic versus human errors.

The interaction between defendant vehicle type and information treatment tests our policy intervention hypothesis. If information emphasizing tort law's preventive function reduces the gap between autonomous and human defendant judgments, this suggests that framing liability as a safety incentive mechanism rather than purely compensatory affects how people evaluate autonomous vehicle responsibility. In addition, we examine interactions between the treatment indicators and key scenario characteristics. This allows us to analyze how bias against autonomous vehicles varies across combinations of treatment conditions and scenario features, such as potential accident preventability and autonomous vehicle performance.

Secondary Outcomes

Secondary Outcomes (end points)
Judgment Confidence Measures

For both responsibility allocation and non-pecuniary damages awards, we measure participants' reported confidence in their judgments on 0-100% continuous scales. These measures assess whether participants experience greater decision uncertainty for autonomous versus human defendant cases, which has implications for jury deliberation dynamics and settlement negotiation patterns even if average judgments appear similar.

Support for the Preventive Function of Tort Law

For participants in the Questions Condition, we measure performance on the two multiple-choice comprehension items testing support for the preventive function of tort law. This manipulation check verifies whether the engagement manipulation successfully increased processing depth and enables instrumental variables analysis treating support question as an endogenous mediator.

Baseline Autonomous Vehicle Attitudes

Pre-treatment measures include self-reported AI knowledge (5-point scale), AI interest level (5-point scale), autonomous vehicle feature usage experience (binary), and autonomous vehicle feature usage frequency (4-point scale). These measures enable analysis of whether pre-existing autonomous vehicle attitudes predict liability judgments and moderate treatment effects.

Scenario-Level Manipulation Checks

For selected scenarios, we measure participants' perceptions of accident preventability and vehicle performance characteristics to verify that manipulated features were perceived as intended. These measures test whether autonomous vehicle defendants described as having superior accident avoidance capabilities are indeed perceived as more technologically advanced than human drivers.
Secondary Outcomes (explanation)
Confidence measures provide insight into decision processes beyond point estimates. Lower confidence for autonomous defendant cases would suggest that current liability frameworks provide insufficient guidance for evaluating algorithmic decisions, even if average fault allocations appear unbiased. This finding would indicate need for doctrinal development rather than simply correcting mean bias.

Attitudinal support questions enable analysis of treatment mechanisms. If the Questions Condition produces stronger effects than information-only exposure, this indicates that active processing is necessary for information to shift liability judgments. If attitudinal support questions mediate treatment effects, this suggests that inducing to think about the preventive role of tort law could enhance intervention effectiveness.

Baseline attitude measures test whether autonomous vehicle bias reflects domain-general technology attitudes or liability-specific concerns. If AI attitudes predict responsibility allocation but do not moderate treatment effects, this suggests that bias stems from stable technology preferences rather than misunderstanding of liability objectives. Conversely, if treatment effects are stronger for individuals with initially negative AI attitudes, this indicates that framing interventions can overcome prior dispositions.

Experimental Design

Experimental Design
This pre-registered online survey experiment is conducted with 4,200 adults in South Korea aged 20 to 60, recruited from a professional online panel. Sample sizes are allocated across age groups to reflect the underlying population structure. Each participant is individually and randomly assigned to one of four treatment arms in a 2×2 factorial design that combines information content (general information about civil litigation vs. information emphasizing the preventive function of tort law) and the presence or absence of support questions about the preventive role of tort law. Participants first answer pre-treatment questions on AI and autonomous vehicles, then receive their randomly assigned information treatment, and subsequently read standardized explanations of key legal concepts such as fully autonomous vehicles, responsibility allocation, and non-pecuniary damages. They are then presented with six traffic accident vignette scenarios, randomly drawn from a set of 30 scenarios that systematically vary the defendant vehicle type, the presence of advanced safety technology for accident prevention (a latest-generation infrared camera), the accident-prediction and avoidance performance of the autonomous vehicle, and the plaintiff’s baseline fault level (medium/low/very low). For each scenario, participants determine the comparative responsibility allocation and the pain and suffering damages amount and report their confidence in these judgments. Finally, they complete a post-treatment questionnaire measuring demographic characteristics, litigation and driving experience, and psychological traits.
Experimental Design Details
Not available
Randomization Method
Random assignment in the experiment is conducted by a computer program. At the point when participants complete the pre-survey and proceed to the information treatment stage, each participant is independently assigned to one of the four treatment conditions. The platform’s randomization algorithm is designed to maintain approximate balance across the four treatment groups, taking into account key demographic characteristics of the Korean population (such as age, gender, and region).
Randomization Unit
Individual participant. Each respondent is independently assigned to treatment with no clustering.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Not applicable (individual-level randomization without clustering)
Sample size: planned number of observations
4,200 participants
Sample size (or number of clusters) by treatment arms
Approximately 1,050 participants per treatment arm:
- Arm 1: Civil Litigation Information Only (1,050 participants)
- Arm 2: Civil Litigation Information + Comprehension Questions (1,050 participants)
- Arm 3: Preventive Function Information Only (1,050 participants)
- Arm 4: Preventive Function Information + Comprehension Questions (1,050 participants)

Since each participant evaluates six vignette scenarios, this yields a total of approximately 25,200 scenario-level observations (about 6,300 per treatment arm).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
KDI School of Public Policy and Management Institutional Review Board
IRB Approval Date
2025-09-01
IRB Approval Number
2025-19
Analysis Plan