CAREER: Virtual Reality Simulations of Moral Decision Making for Autonomous Vehicles

自动驾驶汽车（AV）的事故证明了需要考虑AV使用中固有的道德问题，以及它们将被编程以遵循的道德目标函数。与自动驾驶汽车的合作一直由实用主义模型主导，无论是在算法还是在关于自动驾驶汽车的公共调查框架中。然而，由于方法论问题和公众对功利主义思想的明显抵制，有必要提供替代方法，充分捕捉人类道德判断和决策的灵活性。该项目利用代理-行为-后果（ADC）模型和对人类道德决策的研究，通过使用虚拟现实（VR）和模拟来彻底改变道德人工智能（AI）技术的发展。该项目旨在通过与北卡罗来纳州州立大学图书馆虚拟现实实验室、北卡罗来纳州交通研究与教育研究所（ITRE）、北卡罗来纳州专业工程师（PENC）和北卡罗来纳州科学与数学学院（NCSSM）合作，改善高中和大学教育、跨学科科学研究和交通工程。它有助于㈠提高公众的科学素养和公众对科学和技术的参与; ㈡增加妇女、残疾人和代表性不足的少数群体对研究的参与; ㈢改善科学、技术、工程和数学教育和教育工作者的发展;和（iv）改善社会中个人的福祉。ADC模型借鉴了伦理学，实验哲学，社会心理学和决策神经科学通过将道德判断和决策分解为关于行为人、行为和后果的积极或消极的直觉评价来解释道德判断和决策，并在任何特定情况下将其指定为“高风险”或“低风险”。这些直观的评价联合收割机产生一个积极或消极的道德可接受性的判断。例如，在一个人犯了一个被认为是负面的行为的情况下，整体道德判断（例如，在整条线上改变车道）将被减轻（例如，规避危险）并且结果是好的（例如，事故得以避免）。ADC模型提供了用于表示这种情况的公式（例如，[A+]，[D-][C+] = [MJ+]或积极的道德判断）。该项目开发了新的道德情况下，涉及交通，包括车辆和行人的潜在有害的决定，并测试高中生，专业道德哲学家和外行人没有正式的道德培训的样本的道德判断。道德情境被编程并记录为沉浸式VR环境中的动画，并在高中和大学生以及伦理学家的人群中进行测试。该项目最终以基于ADC模型的原型道德算法为基础，在高保真交通模拟器的模拟视觉环境中与人类决策者进行交互。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Accidents with Autonomous Vehicles (AVs) attest to the need to consider the ethical issues inherent in AVs use, and the ethical goal functions they will be programmed to follow. Work with AVs has been dominated by utilitarian models both in algorithms and framing public surveys regarding AVs. However, due to methodological issues and a pronounced resistance of the general public towards utilitarian thinking, there is a need to offer alternative approaches that adequately capture the flexibility of human moral judgment and decision making. This project utilizes the Agent-Deed-Consequence (ADC) model and research on human moral decision making to revolutionize the development of ethical artificial intelligence (AI) technologies with the use of virtual reality (VR) and simulations. The project aims to improve high-school and college education, interdisciplinary scientific research, and transportation engineering by partnering with the NC State University Libraries’ Virtual Reality Lab, NC State’s Institute for Transportation Research and Education (ITRE), Professional Engineers of North Carolina (PENC), and the North Carolina School of Science and Mathematics (NCSSM). It contributes to (i) increased public scientific literacy and public engagement with science and technology; (ii) increased participation of women, persons with disabilities, and underrepresented minorities in research; (iii) improved Science, Technology, Engineering and Mathematics (STEM) education and educator development; and (iv) improved well-being of individuals in society.The ADC model draws on insights from ethics, experimental philosophy, social psychology and decision neuroscience to explain moral judgments and decisions by breaking them down into positive or negative intuitive evaluations concerning the Agent, the Deed, and the Consequence, and designating them as ‘high-’ or ‘low-stakes’ in any given situation. These intuitive evaluations combine to produce a positive or negative judgment of moral acceptability. For example, the overall moral judgment in a situation in which someone committed a deed that is judged as negative (e.g., changing lanes on a full line) would be mitigated if the agent had good intentions (e.g., circumventing a hazard) and the consequences were good (e.g., an accident was avoided). The ADC model provides formulas for representing such situations (e.g., [A+], [D-] & [C+] = [MJ+] or positive moral judgment). This project develops new moral situations about potentially harmful decisions involving traffic, including vehicles and pedestrians, and tests moral judgment on samples of high-school students, professional moral philosophers, and lay people with no formal training in ethics. The moral situations are programmed and recorded as animations in an immersive VR setting, and tested in populations of high school and college students as well as ethicists. The project culminates with a prototype ethical algorithm based on the ADC model which interacts with human decision makers in a simulated visual environment in the high-fidelity traffic simulator.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.