Context personalization makes people follow AI financial advice more closely
Annie, Are You Okay? How Style- and Context-Based Personalization Shape AI-Assisted Decision-Making
Human-Computer InteractionArtificial Intelligence
Summary
People using AI for financial advice may get different reactions depending on how the AI personalizes its messages. The authors found that when the AI changed its advice based on the person's situation, people were more likely to reconsider their stock choices and move towards the AI's suggestions. However, this didn't make people think the AI was more trustworthy or smarter. People who knew less about stocks were more influenced, even if they thought the AI was sometimes wrong. This shows personalized AI can steer decisions subtly among choices that are all reasonable.
What this means in practice
- •For financial advisors: Adjust AI tools that inform clients about investment options to tailor advice based on client context, increasing client reconsideration of choices.
- •For ux designers for decision support: Design AI interfaces that personalize messages contextually to steer users’ decisions subtly without overemphasizing AI credibility.
Authors
Hasibur Rahman, Benjamin R. Cowan, Smit Desai
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.