In recent years, lawsuits involving autonomous vehicle technology have moved from hypothetical thought experiments to real courtroom disputes. Cases involving Tesla’s Autopilot system, Waymo’s self-driving vehicles, and the fatal 2018 Uber autonomous vehicle crash have drawn national attention and raised a question that courts will increasingly confront as artificial intelligence (AI) becomes more embedded in everyday life: when humans and machines share control, who gets the blame when something goes wrong?
That question extends far beyond self-driving cars. AI is already influencing decisions in healthcare, employment, financial services, transportation, and countless other industries. As AI-related disputes continue to emerge, litigators will face a new challenge: understanding how jurors think about responsibility when both a person and an algorithm contribute to an outcome.
Research on autonomous vehicles provides some of the earliest—and most developed—insights into how jurors may approach that challenge.
AI Can Become a Magnet for Blame
One of the most consistent findings in the autonomous vehicle literature is that people often hold AI systems to a different standard than human actors.
In a series of experiments, De Freitas and colleagues found that participants were more likely to assign liability to the manufacturer of a self-driving vehicle—even when another human driver was clearly responsible for causing the crash (De Freitas et al., 2025). The researchers concluded that autonomous vehicles attract attention because they are novel and highly visible, leading people to focus on what the technology could have done differently rather than on the actions of the at-fault driver.
In other words, AI can become a focal point for blame regardless of whether it was the primary cause of the incident.
The study also identified an important driver of this effect: trust. Participants who expressed lower levels of trust in self-driving technology were significantly more likely to believe the autonomous vehicle should have acted differently and were therefore more willing to assign liability to its manufacturer (De Freitas et al., 2025).
For litigators, this finding raises an important consideration. In cases involving AI, jurors may not evaluate responsibility solely through a traditional negligence framework. The presence of advanced technology may itself become a focal point of blame.
Shared Control Often Means Shared Responsibility
Many AI systems do not operate independently. Instead, they function alongside human decision-makers who retain varying degrees of oversight and control.
Research suggests that jurors recognize this complexity but may struggle to identify a single responsible party. In a study examining crashes involving semi-autonomous vehicles, Zhai and colleagues found that participants frequently divided responsibility between the driver and the vehicle manufacturer when both contributed to an accident (Zhai et al., 2023). Even when human error played a substantial role, respondents continued to assign meaningful responsibility to the company that designed the automated system.
Interestingly, the type of human error appeared to matter less than the fact that both human and machine were involved. Whether the driver was distracted, inattentive, or actively engaged in improper conduct, participants generally viewed responsibility as shared once they perceived control as shared.
As AI-assisted decision-making becomes more common, litigants should expect similar dynamics to emerge in other contexts. Whether the case involves a medical diagnostic tool, an automated hiring platform, or a predictive analytics system, jurors may view responsibility as shared whenever humans and machines are working together.
What Companies Say About AI Matters
The research also highlights the importance of corporate messaging.
Stewart and Gallegos found that participants assigned greater responsibility to manufacturers when advertising materials suggested that self-driving systems required little or no human supervision (Stewart & Gallegos, 2025). When companies appeared to promise a level of autonomy that exceeded the technology’s actual capabilities, jurors became more willing to hold the manufacturer accountable after a crash or traffic violation.
This finding suggests that future AI litigation may involve much more than technical evidence. Marketing materials, public statements, product demonstrations, training materials, and investor communications could all influence how jurors evaluate whether a company appropriately represented the capabilities and limitations of its technology.
As companies race to differentiate themselves in an increasingly competitive AI marketplace, the tension between innovation-focused marketing and realistic expectations may become an increasingly important issue in litigation.
Juror Attitudes Toward AI May Shape Outcomes
Research also suggests that perceptions of AI are influenced by more than case facts alone.
Studies examining attitudes toward artificial intelligence have found that comfort with technology, trust in AI systems, and willingness to adopt new technologies are among the strongest predictors of positive attitudes toward AI (Kaya et al., 2024; Park & Woo, 2022). Individuals who are less familiar with technology or more anxious about AI tend to view the technology more skeptically.
Importantly, these factors appear to be more predictive than demographics alone. While some demographic characteristics correlate with AI attitudes, researchers have found that familiarity with technology and technology-specific anxieties often play a larger role in shaping perceptions (Kaya et al., 2024).
For trial teams, this presents both a challenge and an opportunity. Traditional demographic indicators may reveal relatively little about how jurors will respond to AI-related evidence. Instead, understanding prospective jurors’ experiences with technology, automation, and AI may provide more meaningful insight into how they will evaluate responsibility.
The research also suggests that attitudes toward AI are highly context-dependent. People tend to be comfortable with AI performing routine, data-intensive tasks but are more skeptical when AI is involved in decisions that feel uniquely human, such as hiring, counseling, or medical care (Schepman & Rodway, 2020). As a result, jurors’ reactions may depend not only on whether AI was involved, but on the specific role it played.
What This Means for Litigators
As AI-related disputes become more common, litigators will need to think beyond the technology itself and consider how jurors perceive it. The emerging research suggests that jurors may approach AI with assumptions and concerns that differ from those they bring to more traditional products or decision-making systems.
Three considerations stand out:
- AI may attract scrutiny simply because it is novel. Even when a human actor is primarily responsible for an outcome, jurors may focus on the role of the technology and imagine ways it could have prevented the harm.
- Cases involving AI are likely to generate complex questions about shared responsibility. When both a person and an algorithm contribute to a decision, jurors may be reluctant to assign blame exclusively to either party.
- A company’s own messaging about its technology may become critical evidence. Statements suggesting that an AI system can operate independently or with minimal oversight may shape jurors’ expectations and influence how they assess liability when problems arise.
Autonomous vehicle cases are providing an early roadmap for these issues. While the technology at the center of future disputes may differ, the underlying question will remain the same: how do jurors assign responsibility when humans and machines share control? Understanding the answer may become an increasingly important component of case strategy as AI-related litigation continues to evolve.
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