Shulman, Jeffrey DSmith, Evelyn Olivia2024-09-092024-09-092024Smith_washington_0250E_26726.pdfhttps://hdl.handle.net/1773/51819Thesis (Ph.D.)--University of Washington, 2024Artificial Intelligence (AI) systems are widely adopted by firms to automate tasks such as recommending content, creating advertisements, and conducting marketing research. The primary objective of this research is to broaden our understanding of the interactions among firms, consumers, and AI. This objective is achieved through the development and analysis of a game theory model of firm and consumer reactions to an algorithmic policy, and through the analysis of portrait graphics created by generative AI. Modeling the strategic behaviors of content firms and consumers, I show that an algorithmic constraint requiring recommendation equality between groups can benefit consumers from both groups, increase AI learning investment, or inadvertently harm the consumers it aims to protect. Using generative AI as an example, I empirically show how AI exhibits single-identity and intersectional biases in the depiction of portraits for different occupations. The research extends the marketing literature through theoretical contributions and provides implications of marketing decisions for firms and users interacting with AI systems.application/pdfen-USCC BYArtificial IntelligenceContent RecommendationEmpirical AnalysisGame TheoryGenerative AIQuantitativeMarketingEconomicsStatisticsBusiness administrationThesis