financial advisors

Abstract: Large language models (LLMs) are increasingly used to support consequential decisions, yet their reliability under equivalent representations of uncertainty remains unclear. Existing work has documented prompt sensitivity, cognitive biases, and confidence miscalibration, but it is still unknown whether high-stakes advice changes when the same probability is expressed through complementary gain and loss frames. We introduce FrameBench-HPS, a controlled benchmark of 2,880 prompts derived from 240 high-stakes scenarios across healthcare, finance, education/career, and legal/consumer-rights domains. Across seven LLMs, gain-loss framing changes the recommendation category in 32.4% of complete groups, with most changes involving uncertainty rather than direct reversals. Human evaluation confirms corresponding shifts in perceived risk and directiveness. Robustness experiments show that framing effects persist under conversational paraphrases and exceed ordinary repeated-generation variability. Probability canonicalization substantially reduces framing sensitivity, identifying probability representation as a practical target for improving consistency in high-stakes LLM decision support.

Abstract: AI advising systems are typically designed to always provide a response. Recent work has focused on developing systems that withhold advice in some instances, such as when advice quality is questionable, to improve the overall quality of guidance when it is given; a design paradigm we refer to as AI abstention. While substantial work has focused on optimizing when models should abstain, little is known about how users respond to systems that abstain. In a preregistered experiment, participants made repeated financial decisions, assisted by one of two advising regimes: an AI that advises selectively or one that advises on every decision. We find that the same AI abstention regime produces differential effects depending on the user: it increases advice acceptance among users with high perceived expertise and tends to decrease it among users with low perceived expertise. This differential effect is accounted for by the cognitive burden users face when repeatedly reconciling AI recommendations with their own judgment; AI abstention appears to relieve this burden among users with high perceived expertise, and in doing so increases their receptivity to advice. Further, AI abstention mitigates the decline in advice acceptance observed among expert users over repeated interactions. Exploratory evidence also suggests it increases appropriate acceptance where advice would improve the decision, increasing the downstream accuracy of perceived experts. The AI abstention regime is thus a first-order design property of AI advisory systems, and one that must be calibrated to the users a system serves.

Abstract: As artificial intelligence (AI) increasingly supports human decision-making in high-stakes domains, designing effective human-AI collaboration becomes critical. Successful collaboration requires humans to reflect on decision-making processes and adjust their strategies accordingly. Current research typically investigates interventions, such as explainable AI, that target reflection during the decision-making process (reflection-in-action). Less attention has been given to retrospective examination of decisions (reflection-on-action). This work explores reflection-on-action by studying the impact of feedback on reliance behavior and human-AI team performance. Specifically, we investigate feedback interventions targeting overrides of AI advice through a behavioral study (N=244). We find that such feedback influences reflection-on-action, while unidirectional feedback risks reducing subsequent reflection-in-action. Furthermore, we identify opposing shifts in reliance behavior that leave overall human-AI team performance unchanged but show context-dependent effects. Balanced feedback did not show these risks, preserving both concurrent and retrospective reflection, offering insights for designing for appropriate reliance on AI.

Abstract: Bipolar disorder is strongly associated with financial instability. We examine how different interventions motivate individuals with bipolar disorder to share financial data with others. This approach can inform the development of tools for digital monitoring and intervention designed to promote financial stability in this population. 500 individuals with BD completed a pre-registered factorial vignette survey to examine level of comfort with hypothetical scenarios involving third-party financial interventions during symptomatic and euthymic periods. Scenario components were systematically varied between third-party actors, mood states, and intervention types. Participants rated sharing comfort on a 0-10 point scale. Multilevel models tested differences alongside clinical and financial histories, relational trust, and personality. Participants were most comfortable involving care partners in financial planning. They were more comfortable with temporary spending restrictions during symptomatic states than euthymic periods, underscoring the importance of accurate mood detection for intervention delivery. Prior financial help-seeking behavior and higher relational trust predicted greater comfort. Bankruptcy experience --- declared by 11.4% and considered by 31.7% --- was associated with increased comfort with spending restrictions. Individuals with psychiatric advance directives (8%) were significantly more comfortable sharing spending behaviors than those without. Comfort with financial interventions was higher among those with prior financial challenges or help-seeking histories. Participants distinguished between symptomatic and euthymic periods, favoring targeted, time-limited restrictions over general monitoring. These findings extend prior work on financial data sharing for illness self-management.

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.

Abstract: This paper proposes an investment strategy through resource allocation into Kalshi Crypto Event Contracts in order to effectively hedge exposure to spot asset volatility. Using Bitcoin as a proof of concept, we treat corresponding Kalshi markets on the asset's future price as option contracts, and through construction of different portfolio allocations present a framework for which event contracts can be effectively utilized by retail investors in order to control risk framework without reducing underlying exposure to spot volatility. We showcase this information through analysis of three separate Kalshi event contracts, all relating to a different underlying market regime (i.e bullish, bearish, flat), and provide a comprehensive risk profile for each regime. Results show that a dynamic Kalshi/Bitcoin hedging strategy outperforms more simplistic portfolios across all regimes, supporting our initial hypothesis. We discuss further considerations which can erode profits in the future (i.e. fees), and provide direction for future research.

Abstract: Measuring the preferences of human users remains a fundamental challenge of AI alignment. Existing elicitation approaches struggle to efficiently discover multidimensional preferences or accurately ground these inferences in domain knowledge. To address this, we introduce GUIDE, an LLM-driven elicitation architecture that infers user preferences through conversations by combining Bayesian adaptive sampling for question selection and symbolic representation learning to initialize domain-specific preference models. GUIDE generalizes adaptive sampling to diverse elicitation questions through an extensible type system of transforms on a parameterized preference state. GUIDE produces domain-specific preference representations through an initialization process using symbolic rule-based learning to capture world knowledge and set priors over preference dimensions grounded in data about decision alternatives. The architecture provides observability and steerability to facilitate deployment and analyze elicitation processes. In silico experiments on investment portfolio optimization demonstrate that GUIDE improves cold-start and minimizes recommendation regret consistently within early elicitation interactions across user personas compared to prior work, LLM-only baselines, and ablated GUIDE versions.