红灯，灰色地带：自动驾驶伦理的多视角交互式叙事
Red Light, Grey Zone: A Multi-Perspective Interactive Narrative for Autonomous Driving Ethics
AI总结：
研究自动驾驶伦理这一公共问题，提出多视角交互式叙事方法，通过“红灯，灰色地带”原型展开研究。经探索性用户研究发现，该方法能支持非专家对人工智能系统中的问责、证据和治理进行反思，尤其在责任判断等方面有积极效果。
AI中文摘要：
自动驾驶伦理不仅是专家关注的问题，也是涉及风险、责任和治理的公共问题。非专家在具体事件中往往难以解读这些问题，尤其是责任分散在多个利益相关者身上时。本文研究交互式叙事作为一种面向公众的方法，以引发对自动驾驶的情境化伦理反思。我们展示了受现实世界自动驾驶事件启发的基于网络的多视角交互式叙事原型“红灯，灰色地带”。该原型邀请参与者比较利益相关者的观点，检查场景材料，并在面对伦理模糊性时做出责任判断。我们报告了一项探索性用户研究（N = 12），研究非专家对该原型的不同反应。我们的分析集中在反思的三个维度：伦理认知、以责任为重点的批判性思维和多视角推理。探索性前后结果显示，完成预期利益相关者比较过程的参与者在以责任为重点的批判性思维方面自我报告的转变最为强烈，而伦理认知和多视角推理呈现出积极的趋势。定性研究结果进一步表明参与者如何思考安全与市场权衡、责任模糊性、透明度与隐私以及治理差距。参与者还利用利益相关者比较来确证证据，并且在许多情况下，将责任判断从单一行为者的指责扩大到对问责制的更分散解释。总体而言，该研究表明多视角交互式叙事可能支持非专家对人工智能系统中的问责制、证据和治理进行反思。
英文摘要：
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.
