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This study reveals that context-based personalization in generative AI can subtly steer financial decision-making by increasing reconsideration, even when users do not perceive the system as more trustworthy or intelligent. The research highlights a critical governance challenge where personalized interactions influence choices among defensible options with minimal evaluative traces from the user. These findings underscore the need for careful design and oversight of AI systems that provide decision support in sensitive domains like finance.
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Annie, Are You Okay? How Style- and Context-Based Personalization Shape AI-Assisted Decision-Making

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arXiv:2609.24644v1 Announce Type: cross Abstract: As people turn to generative AI for financial advice, these systems can personalize how they communicate and what they say. Whether these forms of personalization shape decisions differently remains unclear. We conducted a preregistered 2 x 2 between-subjects factorial experiment (N=240): participants ranked three comparably viable stocks, discussed them with an AI, and reranked them. Participants perceived both forms of personalization, but only context-based personalization reliably changed ranking behavior: it increased reconsideration and moved rankings toward the AI's assigned recommendation. Participants felt more influenced without judging the AI as more correct, trustworthy, intelligent, likeable, or high-quality. Those initially farther from its recommendation moved more toward it while judging its advice less correct; exploratory analyses suggest greater susceptibility among lower-expertise participants. These findings show how personalized AI can steer decisions among defensible options with only a minimal evaluative trace, raising concerns for the design and governance of personalized decision support.

Title:Annie, Are You Okay? How Style- and Context-Based Personalization Shape AI-Assisted Decision-Making

Abstract:As people turn to generative AI for financial advice, these systems can personalize how they communicate and what they say. Whether these forms of personalization shape decisions differently remains unclear. We conducted a preregistered 2 x 2 between-subjects factorial experiment (N=240): participants ranked three comparably viable stocks, discussed them with an AI, and reranked them. Participants perceived both forms of personalization, but only context-based personalization reliably changed ranking behavior: it increased reconsideration and moved rankings toward the AI's assigned recommendation. Participants felt more influenced without judging the AI as more correct, trustworthy, intelligent, likeable, or high-quality. Those initially farther from its recommendation moved more toward it while judging its advice less correct; exploratory analyses suggest greater susceptibility among lower-expertise participants. These findings show how personalized AI can steer decisions among defensible options with only a minimal evaluative trace, raising concerns for the design and governance of personalized decision support.

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2609.24644 [cs.HC]

(or arXiv:2609.24644v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2609.24644 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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