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.
