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 consistently end up on the losing side of AI decisions. I propose and evaluate two strategies to reduce outcome homogenization: model multiplicity and randomization. Part…