Red Light, Grey Zone: A Multi-Perspective Interactive Narrative for Autonomous Driving Ethics

2026-07-17

AI Summary

This study addresses the public’s difficulty in navigating moral dilemmas and assigning responsibility in autonomous vehicle ethics incidents, particularly under conditions of ambiguous risk, accountability, and governance. To bridge this gap, the work proposes an innovative, web-based interactive narrative prototype grounded in real-world cases, enabling non-expert users to compare stakeholder perspectives, evaluate evidence, and make responsibility judgments through scenario-based simulations. Drawing on mixed-methods user research (N=12), the investigation focuses on three dimensions: ethical awareness, critical reasoning about responsibility, and multi-perspective inference. Findings indicate that after engaging with the prototype, participants demonstrated significantly enhanced critical thinking regarding responsibility, exhibited a stronger preference for distributed accountability, and engaged more deeply with systemic issues such as safety–market trade-offs, privacy concerns, and governance gaps—thereby shifting public discourse from simplistic blame attribution toward nuanced, systemic ethical reflection.

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.

Problem

• autonomous driving ethics
• public engagement
• responsibility attribution
• ethical ambiguity
• multi-stakeholder perspectives

Innovation

• interactive narrative
• multi-perspective reasoning
• autonomous driving ethics
• public engagement
• responsibility attribution