UC Santa Cruz

In the context of artificial intelligence (AI) or machine learning (ML), we speak of the "alignment" of an AI system's behavior with human goals, values, and ethical principles. "The alignment problem" has proven challenging, and as the capabilities and applications of AI rapidly advance, the shortcomings of standard solutions are increasingly consequential. This dissertation focuses on an often overlooked but critically important complication to the alignment problem: Socially-consequential AI systems affect their environment (involving, for example, human populations) and are therefore subject to dynamical feedback driven by other agents. We address three central questions: (1) As intelligent agents adapt to each other, does a system aligned using current leading approaches remain aligned? (2) Can we anticipate and utilize adaptive agents' reactions to data-driven policy to achieve aligned objectives dynamically? (3) How can we guarantee alignment for AI systems that interact with complex, multiagent environments that are difficult to model or predict? We will address these questions using the theoretical framework and experimental tools of machine learning---integrating concepts from dynamical systems, evolutionary game theory, constrained optimization, and control theory. We hope to demonstrate that a dynamical systems approach to deployed AI is not only necessary but beneficial to the goal of alignment.