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Model development in AI is shaped by developer decisions. While there is significant research on the opportunities and risks of multiplicity, little attention has been paid to how developer decisions impact multiplicity. My thesis focuses on (a) introducing broader frameworks to better situate and analyze developer decisions in AI, (b) identifying theoretical connections to characterize the influence of these decisions on multiplicity, and (c) operationalizing these insights across various applications, thus building responsible AI models with multiplicity.