Single error sinks trust in AI financial advisors, clear explanations rebuild it

The study points out that explanations do not need to be technically elaborate to be effective. Instead, what matters is timeliness, clarity, and relevance to the user’s decision-making needs. By acknowledging the error and offering a rationale that users can easily grasp, the AI advisor was able to regain much of the lost confidence.

In a world where artificial intelligence is rapidly entering finance and personal decision-making, a new study warns that user trust in AI advisors is both powerful and fragile. Researchers examined how trust forms, collapses, and recovers when an AI-based financial advisor makes an error.

Their peer-reviewed paper, “Trust Formation, Error Impact, and Repair in Human–AI Financial Advisory: A Dynamic Behavioral Analysis,” published in Behavioral Sciences, provides some of the clearest evidence yet that while people initially place significant confidence in machine advisors, that trust can quickly erode after a single visible mistake. The research also shows that simple, well-timed explanations can help rebuild that trust.

Why trust in AI advisors rises quickly but falls faster

The researchers explore how individuals compare AI and human advisors in routine financial decisions. In the first experiment involving 189 participants, the study found that people tend to give AI advisors an initial advantage. Trust scores, satisfaction levels, and willingness to follow the AI’s recommendations were all higher than those for human experts at the outset.

This initial surge of confidence reflects what the authors call “algorithm appreciation”, a belief that data-driven systems are more objective and consistent than people. However, the research demonstrates that this confidence is volatile. When participants in the second experiment, which involved 294 individuals, encountered a single inaccurate recommendation from the AI, their trust declined sharply. The drop in trust was much steeper than what typically occurs in human-to-human advisory interactions.

The results highlight a paradox at the core of human-AI collaboration: while users may start out more receptive to machines than to people, that goodwill can evaporate after just one mistake.

How explanations can restore user confidence

The study assesses whether explanations can reverse trust erosion. After exposing participants to the AI’s error, the researchers introduced a plain, concise explanation for why the recommendation had failed. The presence of an explanation significantly improved trust scores in subsequent rounds of decision-making, indicating that people respond positively when they understand why a mistake occurred.

The study points out that explanations do not need to be technically elaborate to be effective. Instead, what matters is timeliness, clarity, and relevance to the user’s decision-making needs. By acknowledging the error and offering a rationale that users can easily grasp, the AI advisor was able to regain much of the lost confidence.

Notably, the research also found that users with higher levels of financial literacy reacted more strongly both to the initial error and to the subsequent explanation. This suggests that explanations can play an especially important role in repairing trust among more knowledgeable users who scrutinize advice more closely.

Designing AI systems that anticipate human reactions

The findings have far-reaching implications for developers of financial technology and other AI-driven services. According to the authors, designers should not assume that strong initial trust will persist. Instead, they should plan for inevitable mistakes and embed mechanisms to address them transparently.

The study recommends that AI systems provide clear and upfront information about their capabilities and limitations, so that users have realistic expectations. When errors do occur, AI advisors should acknowledge them promptly and supply explanations tailored to the user’s knowledge level. Such strategies can prevent temporary lapses from becoming long-term distrust.

The authors also argue that product teams must collaborate with behavioral scientists to shape the communication style of explanations. Focusing on practical guidance and next steps for lower-literacy users while offering more technical detail for experienced users can make trust repair efforts more effective across diverse audiences.

Implications for the future of Human–AI collaboration

The research underscores that building and sustaining trust in AI advisors is not just a technical challenge but also a behavioral and ethical one. The volatility of user trust revealed in the experiments highlights why companies deploying AI in sensitive areas such as finance, health, and law cannot rely solely on model accuracy.

However, the study also acknowledges its own limitations, including its reliance on simulated advisory tasks, short time frames, and self-reported attitudes. The authors call for future research in real-world settings to test whether these trust dynamics hold over longer periods and across more complex decision environments.