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Standard Grant: Ethical Algorithms in Autonomous Vehicles

Award Abstract #1734521
This project supports a team of researchers, including experts in moral philosophy and transportation engineering, who plan to address ethical issues that arise in the development of computer algorithms for autonomous (self-driving) vehicles. The project will address concerns over the expression of ethical values in self-driving vehicle when (for example) the vehicle detects an imminent and unavoidable crash and must select among navigation options, such as colliding with a crowded bus or with a lone motorcyclist. It aims to develop algorithmic representations of ethical decision-making frameworks for autonomous vehicles, and then model these frameworks over a range of contexts for their effect on risk management in traffic networks and for their expected public health impact. The overarching aim of the project is to promote principled approaches to ethical decision-making in complex, autonomous systems, and to prepare designers and engineers to encode ethical behavior into such systems. Research mentoring is to be coordinated through the host institution that will enable undergraduate and graduate engineers to participate in the project. A summer intensive will bring together students from diverse backgrounds to work with the project team and invited scholars. A postdoctoral fellow will conduct research on the ethics of autonomous vehicles, and assist in project administration.

The project has two specific aims. It aims to develop ethical algorithms that can be converted into computer code for use in autonomous vehicles. Team members will review the literature on existing decision-making for autonomous vehicles, and then develop conceptual, algorithmic representations of ethical behavior that can be converted into computer code. They will also model changes in traffic risk profiles and expected public health impact of these algorithms; specifically, they will apply injury severity and crash frequency modeling using algorithms developed in meeting the first aim in a range of traffic contexts and market penetration scenarios. They will also develop a model for the expected public health benefits from these algorithms using model of resource and time-based triaging as a starting point. Both aims support training of scholars and practitioners sensitive to the ethical implications of autonomous vehicles. These pedagogical aspects have been designed to promote diverse interactions between STEM students and practitioners, and they will serve to improve STEM education and educator development.