A closer look at how large language models trust humans: patterns and biases

• URL: http://arxiv.org/abs/2504.15801v1
• Date: Tue, 22 Apr 2025 11:31:50 GMT
• Title: A closer look at how large language models trust humans: patterns and biases
• Authors: Valeria Lerman, Yaniv Dover,
• Abstract summary: Large language models (LLMs) and LLM-based agents increasingly interact with humans in decision-making contexts.<n>LLMs rely on some sort of implicit effective trust in trust-related contexts to assist and affect decision making.<n>We study whether LLMs trust depends on the three major trustworthiness dimensions: competence, benevolence and integrity of the human subject.<n>We find that in most, but not all cases, LLM trust is strongly predicted by trustworthiness, and in some cases also biased by age, religion and gender.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language models (LLMs) and LLM-based agents increasingly interact with humans in decision-making contexts, understanding the trust dynamics between humans and AI agents becomes a central concern. While considerable literature studies how humans trust AI agents, it is much less understood how LLM-based agents develop effective trust in humans. LLM-based agents likely rely on some sort of implicit effective trust in trust-related contexts (e.g., evaluating individual loan applications) to assist and affect decision making. Using established behavioral theories, we develop an approach that studies whether LLMs trust depends on the three major trustworthiness dimensions: competence, benevolence and integrity of the human subject. We also study how demographic variables affect effective trust. Across 43,200 simulated experiments, for five popular language models, across five different scenarios we find that LLM trust development shows an overall similarity to human trust development. We find that in most, but not all cases, LLM trust is strongly predicted by trustworthiness, and in some cases also biased by age, religion and gender, especially in financial scenarios. This is particularly true for scenarios common in the literature and for newer models. While the overall patterns align with human-like mechanisms of effective trust formation, different models exhibit variation in how they estimate trust; in some cases, trustworthiness and demographic factors are weak predictors of effective trust. These findings call for a better understanding of AI-to-human trust dynamics and monitoring of biases and trust development patterns to prevent unintended and potentially harmful outcomes in trust-sensitive applications of AI.

Related papers

• Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators and users, modelling their strategic choices under different regulatory scenarios.<n>Our research identifies emerging behaviours of strategic AI agents, which tend to adopt more "pessimistic" stances than pure game-theoretic agents.
  arXiv  Detail & Related papers  (2025-04-11T15:41:21Z)
• Measurement of LLM's Philosophies of Human Nature [113.47929131143766]
  We design the standardized psychological scale specifically targeting large language models (LLM)<n>We show that current LLMs exhibit a systemic lack of trust in humans.<n>We propose a mental loop learning framework, which enables LLM to continuously optimize its value system.
  arXiv  Detail & Related papers  (2025-04-03T06:22:19Z)
• Measuring and identifying factors of individuals' trust in Large Language Models [0.0]
  Large Language Models (LLMs) can engage in human-looking conversational exchanges.<n>We introduce the Trust-In-LLMs Index (TILLMI) as a new framework to measure individuals' trust in LLMs.
  arXiv  Detail & Related papers  (2025-02-28T13:16:34Z)
• Trusting Your AI Agent Emotionally and Cognitively: Development and Validation of a Semantic Differential Scale for AI Trust [16.140485357046707]
  We develop and validated a set of 27-item semantic differential scales for affective and cognitive trust. Our empirical findings showed how the emotional and cognitive aspects of trust interact with each other and collectively shape a person's overall trust in AI agents.
  arXiv  Detail & Related papers  (2024-07-25T18:55:33Z)
• MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models [51.19622266249408]
  MultiTrust is the first comprehensive and unified benchmark on the trustworthiness of MLLMs. Our benchmark employs a rigorous evaluation strategy that addresses both multimodal risks and cross-modal impacts. Extensive experiments with 21 modern MLLMs reveal some previously unexplored trustworthiness issues and risks.
  arXiv  Detail & Related papers  (2024-06-11T08:38:13Z)
• Can Large Language Model Agents Simulate Human Trust Behavior? [81.45930976132203]
  We investigate whether Large Language Model (LLM) agents can simulate human trust behavior. GPT-4 agents manifest high behavioral alignment with humans in terms of trust behavior. We also probe the biases of agent trust and differences in agent trust towards other LLM agents and humans.
  arXiv  Detail & Related papers  (2024-02-07T03:37:19Z)
• What Large Language Models Know and What People Think They Know [13.939511057660013]
  Large language models (LLMs) are increasingly integrated into decision-making processes.<n>To earn human trust, LLMs must be well calibrated so that they can accurately assess and communicate the likelihood of their predictions being correct.<n>Here we explore the calibration gap, which refers to the difference between human confidence in LLM-generated answers and the models' actual confidence, and the discrimination gap, which reflects how well humans and models can distinguish between correct and incorrect answers.
  arXiv  Detail & Related papers  (2024-01-24T22:21:04Z)
• A Diachronic Perspective on User Trust in AI under Uncertainty [52.44939679369428]
  Modern NLP systems are often uncalibrated, resulting in confidently incorrect predictions that undermine user trust. We study the evolution of user trust in response to trust-eroding events using a betting game.
  arXiv  Detail & Related papers  (2023-10-20T14:41:46Z)
• Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods [12.641141743223377]
  Trust has emerged as a key factor in people's interactions with AI-infused systems. Little is known about what models of trust have been used and for what systems. There is yet no known standard approach to measuring trust in AI.
  arXiv  Detail & Related papers  (2022-04-30T07:34:19Z)
• Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI [55.4046755826066]
  We discuss a model of trust inspired by, but not identical to, sociology's interpersonal trust (i.e., trust between people) We incorporate a formalization of 'contractual trust', such that trust between a user and an AI is trust that some implicit or explicit contract will hold. We discuss how to design trustworthy AI, how to evaluate whether trust has manifested, and whether it is warranted.
  arXiv  Detail & Related papers  (2020-10-15T03:07:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.