It is shown that human acceptance of AI advice exhibits asymmetric algorithm aversion that depends on the advice the AI provides, which is important because an inconsistent attitude towards accepting AI advice may lead to systematic biases in human-AI collaboration that may defeat the very purpose of using AI.
Abstract
We examine human decision-making in the presence of Artificial Intelligence (AI) advice in the context of Fintech. Through four studies (two randomized controlled experiments, a field study, and a qualitative interview study), we show that human acceptance of AI advice exhibits asymmetric algorithm aversion that depends on the advice the AI provides. In our Fintech setting, investments are commonly assessed using criteria based on both explicit knowledge and tacit knowledge. Human experts believed that AI had the ability to effectively use explicit knowledge but were less confident in its ability to effectively use tacit knowledge (a common perception regarding AI). As a result, AI advice significantly influenced experts’ evaluations when it recommended against investing, as they believed that this advice could have been correctly generated using explicit knowledge criteria alone. However, AI advice had no significant effect on experts’ evaluations when it recommended investing, as they believed that this advice could not have been correctly generated without using both types of knowledge and that AI could not effectively use tacit knowledge. Instead, AI advice to invest served as a trigger for them to examine the investment with a focus on tacit knowledge criteria, which often meant that the human expert’s evaluation did not match AI’s advice. We did not find this same asymmetric pattern for accepting human advice in this context, indicating that the results are not due to loss aversion. Recognizing asymmetric algorithm aversion—that human experts are more likely to accept AI’s advice when it produces one recommendation and less likely when it produces a different recommendation —is important because an inconsistent attitude towards accepting AI advice may lead to systematic biases in human-AI collaboration that may defeat the very purpose of using AI.
With the development of artificial intelligence (AI) in recent years, intelligent recommendation systems have become popular in daily life and are used to help people make decisions. AI recommendation systems will be of use only if consumers are willing to be guided by them. This paper studies consumers' trust in AI-generated recommendations under conditions of AI-assisted decision-making. Based on the literature of algorithm aversion, algorithm appreciation and trust in automation, this paper will present various psychological and cognitive reasons for changes in people's perceptions of algorithms. Research has shown that a sense of personalisation, relatively simple tasks and good explanations (explainable AI) can make people feel more trusted. Based on the above analysis, consumers are more likely to accept algorithms for objective and data-driven applications; however, they may be hesitant about algorithms used in cases of subjective judgment or high-stakes decisions, especially after learning of algorithmic errors. In addition, the above analysis also shows that emotional trust may be a mediator in the intention to delegate decision-making to AI agents. In short, this paper offers a theoretical discussion on algorithmic reliance and proposes strategies to build more transparent and trustworthy AI recommendation systems that can improve the user experience.
Yayi Liu· Frontiers in Humanities and...· 0 citations
INTRODUCTION
Although medical risk calculators are increasingly being used for risk prediction in various contexts, prior research in this area suggests that people may be averse to recommendations from these tools. Moreover, given the rise of artificial intelligence in health care, aversion toward AI may manifest toward risk calculators that use AI/machine learning models. Recent work has suggested that one potential way to combat AI aversion is through explainable AI (XAI), which can make underlying models more transparent.
METHODS
The current study investigated whether these factors would affect public trust, acceptance, and comfort related to recommendations from a risk calculator. Participants were randomized into a 2 (calculator type: statistical vs AI) × 2(model explanation: explained vs not explained) × 2(evaluability: risk reference table vs no table) between-subjects experimental design. They read a hypothetical scenario and received a calculator output with a risk estimate and recommendation before completing measures of trust, comfort, and acceptance.
RESULTS
Analyses revealed main effects of calculator type and explanation: participants who received the AI-based calculator outputs were less likely to trust and be comfortable with the calculator recommendation. Participants who received an explanation of the underlying model were more likely to trust and accept the calculator recommendation. However, there was no effect of XAI on trust, acceptance, and comfort or any other significant interactions.
CONCLUSION
The current study serves as a starting point for research on trust and acceptance of AI-based risk calculators. Our findings support the AI aversion hypothesis and suggest that there needs to be more work to identify how best to explain AI-based calculators to foster trust and comfort more specifically.
Madhuri Ramasubramanian, Brian J. Zikmund-Fisher· Medical decision making· 0 citations
It is shown that a key factor is the error correlation structure between human and AI predictions, and when the AI's prediction errors are negatively correlated with those of the human, the decision maker can construct robust strategies which guarantee improvements in expected utility.
Findings show that financial-advice evaluations are shaped jointly by displayed attribution and message-level communication cues, which position disclosure not as a neutral transparency mechanism, but as an interpretive frame whose accuracy and interaction with message cues can shape trust and reliance.
A. Kapadia, Eshwar Chandrasekharan, Koustuv Saha· 0 citations
While participants rated hedged and unhedged AI as equally trustworthy and likely to be correct, they were significantly less likely to follow hedged advice in a binary choice, and how linguistic markers can be used to calibrate user reliance to model certainty is discussed.
Laura Spillner, Johanna Rockstroh, Nina Wenig et al.· International Conference on...· 0 citations
This study investigates behavioral biases of generative artificial intelligence (AI) models, specifically GPT-4o and Claude-Haiku-4.5, in inventory management using the newsvendor problem. This study compares AI decision-making with human-subject experiments to assess whether large language models (LLMs) replicate human cognitive bias and to identify prompt-design strategies that improve alignment with optimal outcomes.
Controlled newsvendor experiments were conducted with generative AI models, mirroring established human-subject laboratory protocols. Prompt framing was systematically varied across three modifications: removing explicit waste and missed-profit information, simplifying instruction format and providing explicit optimization formulas. Results were benchmarked against normative economic predictions and existing human behavioral findings.
Generative AI exhibits human-like human biases including risk aversion, loss aversion and demand chasing, but exhibits a stronger demand-chasing tendency than human participants. It responds to hypothetical incentives and displays bounded rationality. Prompt design significantly influences decision quality, producing decisions closer to theoretical benchmarks.
This study empirically tests generative AI behavioral biases within a structured operations management experiment. It introduces a replicable methodology, extends findings across two architecturally distinct LLMs from different developers, and demonstrates that deliberate prompt design meaningfully reduces AI decision bias. The study also contributes a conceptual distinction between functionally analogous behavioral patterns and intrinsic psychological dispositions in LLMs, offering a more precise interpretive framework for AI decision-making research in operational contexts.
Jing-Jie Su, Yan Lang, Kay-Yut Chen· Review of Behavioral Economi...· 0 citations