Abstract: When a learning system is deployed, the data it encounters may no longer resemble its training data. The nature of this shift is typically unknown, whether due to changing conditions, adversarial manipulation, or simply a new deployment context. Without assumptions on the shift, reliable prediction is in general impossible, as the test distribution may…
Abstract: Modern AI systems are capable of generating synthetic representations of complex entities—from personalities to creative works—that can increasingly serve as plausible substitutes for the objects themselves. This talk examines the economic and regulatory implications of this shift via two papers. The first, “Artificial Intelligence Clones,” analyzes search and matching when people are represented by…
AI Homogenization in Decision-Making and Alignment ABSTRACT As AI systems become more pervasive, their outputs in both decision-making and generative tasks often lack the diversity expected or desired. This thesis advances our understanding of AI homogenization by evaluating several distinct forms and proposing practical mitigation strategies. Part I studies outcome homogenization, or when certain individuals…
Abstract: When a learning system is deployed, the data it encounters may no longer resemble its training data. The nature of this shift is typically unknown, whether due to changing conditions, adversarial manipulation, or simply a new deployment context. Without assumptions on the shift, reliable prediction is in general impossible, as the test distribution may…
Abstract: Modern AI systems are capable of generating synthetic representations of complex entities—from personalities to creative works—that can increasingly serve as plausible substitutes for the objects themselves. This talk examines the economic and regulatory implications of this shift via two papers. The first, “Artificial Intelligence Clones,” analyzes search and matching when people are represented by…
AI Homogenization in Decision-Making and Alignment ABSTRACT As AI systems become more pervasive, their outputs in both decision-making and generative tasks often lack the diversity expected or desired. This thesis advances our understanding of AI homogenization by evaluating several distinct forms and proposing practical mitigation strategies. Part I studies outcome homogenization, or when certain individuals…
TBD
TBD
TBD
