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The Impact on Society of Autonomous Mobile Robots: Prediction Models and Intervention Strategies

Principal Investigator
Marco Pavone, Assistant Professor of Aeronautics and Astronautics, and by courtesy, Electrical Engineering
Co-Principal Investigators
Rebecca Diamond, Assistant Professor of Economics, Stanford Graduate School of Business
David B. Grusky, Professor of Sociology
Mykel Kochenderfer, Assistant Professor of Aeronautics and Astronautics, and by courtesy, Computer Science
David Lentink, Assistant Professor of Mechanical Engineering
Margaret Levi, Professor of Political Science
Paul Oyer, Professor of Economics, Stanford Graduate School of Business
Grand Challenges
How can we use autonomy to enable future technologies?
How can we ensure that humanity flourishes in the cities of the future?
How do we create synergy between humans and engineered systems?
The goal of this project is to devise methodologies to predict and influence the impact of autonomous mobile robots (in particular, self-driving cars and drones) on society. While the substitution of technology for human labor has been perhaps the most significant driving force of economic progress since the industrial revolution, there is real debate about whether new forms of automation, collectively referred to as “artificial intelligence” (AI), will have the same long-term positive consequences. The key question is: “will AI replace more jobs than it creates, and with what impact on society”? Scholars have diverging opinions, ranging from optimistic to pessimistic. This project proposes to restrict the study of the impact of AI on society to a specific class of AI-enabled robots, namely, autonomous mobile robots (AMR). This is for three main reasons: (1) we hypothesize that such a focus on a clear, well-defined set of technologies will produce actionable results, (2) we believe that AMR might be one of the first forms of advanced AI to have a fast and profound impact on society, and (3) we believe that AMR, being at the leading edge of the new AI-induced economic revolution, will provide a critical early opportunity to better understand—and influence—how AI will play out more generally in societal and moral terms. 
Our technical approach is to model society as the interconnection of three entities, namely (1) societal relations, which includes human and organizational interactions, (2) societal infrastructure, which includes transportation, energy, and the built environment, and (3) societal controls, which encompasses governance arrangements and public policy more broadly. This project then has three objectives: (1) to devise methodologies to predict the impact of AMR on societal infrastructures; (2) to devise methodologies that allow an understanding (and, to some extent, a prediction) of the impact of AMR on societal relations and societal controls (with an emphasis on the labor market); and (3) guided by the models and methods from Objectives 1 and 2, to examine societal control strategies in order to characterize how they might impact and adapt to the possible trajectories of technology development.
The collaboration plan will ensure the dissemination of the project’s findings to a variety of academic and non-academic audiences, and will prepare the next generation of engineers and social scientists to take on the challenges stemming from the interplay between AMR (and, more generally, AI) and society.
The impact of this project will be threefold. First, it will advance the knowledge base in AMR, labor economics, and the behavioral sciences. Second, it will build an intellectual and interdisciplinary community at Stanford (and, by proximity, in Silicon Valley) committed to studying rigorously and independently how best to ensure that AMR will be beneficial to the whole of society. Third, it will generate a set of policy guidelines and proposed societal experiments that could help to inform and constructively influence ideas, conversations, and language around AMR.