# The social dilemma of autonomous vehicles

arXiv: https://arxiv.org/abs/1510.03346 · HTML: https://mlsanity.com/paper/1510.03346/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2015.

## Built on by (most cited first, 30 of 48)

- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model](https://mlsanity.com/paper/2402.10828/) (2024)
- [World Models for Autonomous Driving: An Initial Survey](https://mlsanity.com/paper/2403.02622/) (2024)
- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language Models](https://mlsanity.com/paper/2312.03543/) (2023)
- [Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making](https://mlsanity.com/paper/2102.00625/) (2021)
- [Responsible AI: Gender bias assessment in emotion recognition](https://mlsanity.com/paper/2103.11436/) (2021)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [Attributions toward Artificial Agents in a modified Moral Turing Test](https://mlsanity.com/paper/2406.11854/) (2024)
- [Metaethical Perspectives on 'Benchmarking' AI Ethics](https://mlsanity.com/paper/2204.05151/) (2022)
- [Large-scale moral machine experiment on large language models](https://mlsanity.com/paper/2411.06790/) (2024)
- [Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisions](https://mlsanity.com/paper/2503.00940/) (2025)
- [Many LLMs Are More Utilitarian Than One](https://mlsanity.com/paper/2507.00814/) (2025)
- [Hybrid Approaches for Moral Value Alignment in AI Agents: a Manifesto](https://mlsanity.com/paper/2312.01818/) (2023)
- [Have a break from making decisions, have a MARS: The Multi-valued Action Reasoning System](https://mlsanity.com/paper/2109.03283/) (2021)
- [Moral Dilemmas for Moral Machines](https://mlsanity.com/paper/2203.06152/) (2022)
- [When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data](https://mlsanity.com/paper/2201.07763/) (2022)
- [AI Safety vs. AI Security: Demystifying the Distinction and Boundaries](https://mlsanity.com/paper/2506.18932/) (2025)
- [The social dilemma in artificial intelligence development and why we have to solve it](https://mlsanity.com/paper/2107.12977/) (2021)
- [Using Collision Momentum in Deep Reinforcement Learning Based Adversarial Pedestrian Modeling](https://mlsanity.com/paper/2306.07525/) (2023)
- [On the Need for a Statistical Foundation in Scenario-Based Testing of Autonomous Vehicles](https://mlsanity.com/paper/2505.02274/) (2025)
- [The Science Fiction Science Method](https://mlsanity.com/paper/2508.03430/) (2025)
- [Artificial intelligence in government: Concepts, standards, and a unified framework](https://mlsanity.com/paper/2210.17218/) (2022)
- [On the limits of algorithmic prediction across the globe](https://mlsanity.com/paper/2103.15212/) (2021)
- [Learning Roles with Emergent Social Value Orientations](https://mlsanity.com/paper/2301.13812/) (2023)
- [People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI](https://mlsanity.com/paper/2403.08828/) (2024)
- [Tesla's Autopilot: Ethics and Tragedy](https://mlsanity.com/paper/2409.17380/) (2024)
- [Randomized Classifiers vs Human Decision-Makers: Trustworthy AI May Have to Act Randomly and Society Seems to Accept This](https://mlsanity.com/paper/2111.07545/) (2021)
- [Advising Autonomous Cars about the Rules of the Road](https://mlsanity.com/paper/2209.14035/) (2022)
- [Assessing Trustworthiness of Autonomous Systems](https://mlsanity.com/paper/2305.03411/) (2023)

## Abstract (arXiv)

Autonomous Vehicles (AVs) should reduce traffic accidents, but they will sometimes have to choose between two evils-for example, running over pedestrians or sacrificing itself and its passenger to save them. Defining the algorithms that will help AVs make these moral decisions is a formidable challenge. We found that participants to six MTurk studies approved of utilitarian AVs (that sacrifice their passengers for the greater good), and would like others to buy them, but they would themselves prefer to ride in AVs that protect their passengers at all costs. They would disapprove of enforcing utilitarian AVs, and would be less willing to buy such a regulated AV. Accordingly, regulating for utilitarian algorithms may paradoxically increase casualties by postponing the adoption of a safer technology.