Red Light, Grey Zone: A Multi-Perspective Interactive Narrative for Autonomous Driving Ethics
Computers and SocietyHuman-Computer Interaction
Summary
The authors studied how people who aren't experts think about ethical issues in self-driving cars, especially when lots of different people share responsibility for accidents. They created an interactive online story called Red Light, Grey Zone, where users see the situation from various viewpoints and decide who is responsible. In a small study with 12 participants, the authors found that those who compared different perspectives improved most in thinking about responsibility, while overall understanding of ethics and seeing multiple viewpoints also improved. People reflected on topics like safety, privacy, and unclear responsibility, often moving beyond blaming just one person. The authors suggest that such interactive stories can help everyday people better understand complex ethical questions around AI systems.
Authors
Mengyi Wei, Nianhua Liu, Chenyu Zuo, Liqiu Meng
Abstract
Autonomous driving ethics is not only an expert concern, but also a public issue involving risk, responsibility, and governance. However, non-experts often struggle to interpret these issues in concrete incidents, especially when responsibility is distributed across multiple stakeholders. This paper investigates interactive narrative as a public-facing method for eliciting situated ethical reflection on autonomous driving. We present Red Light, Grey Zone, a web-based, multi-perspective interactive narrative prototype inspired by a real-world autonomous-driving incident. The prototype invites participants to compare stakeholder perspectives, examine scene materials, and make responsibility judgments in the face of ethical ambiguity. We report an exploratory user study (N=12) examining how differently non-experts responded to the prototype. Our analysis focuses on three dimensions of reflection: ethical cognition, responsibility-focused critical thinking, and multi-perspective reasoning. Exploratory pre-post results showed the strongest self-reported shift in responsibility-focused critical thinking among participants who completed the intended stakeholder-comparison process, while ethical cognition and multi-perspective reasoning showed positive directional trends. Qualitative findings further show how participants reflected on safety and market trade-offs, responsibility ambiguity, transparency and privacy, and governance gaps. Participants also used stakeholder comparison to corroborate evidence and, in many cases, broaden responsibility judgments from single-actor blame toward more distributed interpretations of accountability. Overall, the study suggests that multi-perspective interactive narratives may support non-expert reflection on accountability, evidence, and governance in AI-enabled systems.