Why do we Trust Chatbots? From Normative Principles to Behavioral Drivers

• URL: http://arxiv.org/abs/2602.08707v2
• Date: Wed, 11 Feb 2026 13:38:18 GMT
• Title: Why do we Trust Chatbots? From Normative Principles to Behavioral Drivers
• Abstract summary: We argue that the coexistence of competing notions of "trust" under a shared term obscures important distinctions between psychological trust formation and normative trustworthiness.<n>Addressing this gap requires further research and stronger support mechanisms to help users appropriately calibrate trust in conversational AI systems.
• Score: 9.407740029262124
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As chatbots increasingly blur the boundary between automated systems and human conversation, the foundations of trust in these systems warrant closer examination. While regulatory and policy frameworks tend to define trust in normative terms, the trust users place in chatbots often emerges from behavioral mechanisms. In many cases, this trust is not earned through demonstrated trustworthiness but is instead shaped by interactional design choices that leverage cognitive biases to influence user behavior. Based on this observation, we propose reframing chatbots not as companions or assistants, but as highly skilled salespeople whose objectives are determined by the deploying organization. We argue that the coexistence of competing notions of "trust" under a shared term obscures important distinctions between psychological trust formation and normative trustworthiness. Addressing this gap requires further research and stronger support mechanisms to help users appropriately calibrate trust in conversational AI systems.

Related papers

• Trust by Design: Trust Calibration Through Non-Advisory Socratic Dialogue in Conversational Agents [0.0]
  We present CASELy, a conversational agent designed to limit its own authority through non-advisory Socratic dialogue.<n>The agent asks reflective questions grounded exclusively in user input and refuses to provide advice, recommendations, or interpretations.
  arXiv  Detail & Related papers  (2026-09-13T22:19:18Z)
• Verification Without Distrust: Reframing User-Side Oversight as Routine Epistemic Governance in Everyday Human-Chatbot Interaction [0.0]
  We find no detectable association between trust and verification in everyday human-chatbot interaction.<n>Three further user-side practices - refinement, correction, and approval before automated actions - are widely endorsed and positively associated with satisfaction.<n>A medium-to-large satisfaction-control gap shows that effective task outcomes do not produce a felt sense of agency.
  arXiv  Detail & Related papers  (2026-05-31T14:12:11Z)
• Trust as a Situated User State in Social LLM-Based Chatbots: A Longitudinal Study of Snapchat's My AI [0.0]
  Trust is shaped by perceived ability, conversational behavior, human-likeness, transparency, privacy concerns, and trust in the host platform.<n>Our findings show that trust is shaped by perceived ability, conversational behavior, human-likeness, transparency, privacy concerns, and trust in the host platform.
  arXiv  Detail & Related papers  (2026-04-24T10:22:41Z)
• On the Need to Rethink Trust in AI Assistants for Software Development: A Critical Review [16.774993642353724]
  Trust is a fundamental concept in human decision-making and collaboration.<n>Software engineering articles often use the term trust informally.<n> Related disciplines commonly embed their methodology and results in established trust models.
  arXiv  Detail & Related papers  (2025-04-16T19:52:21Z)
• Fostering Trust and Quantifying Value of AI and ML [0.0]
  Much has been discussed about trusting AI and ML inferences, but little has been done to define what that means. producing ever more trustworthy machine learning inferences is a path to increase the value of products.
  arXiv  Detail & Related papers  (2024-07-08T13:25:28Z)
• Common (good) practices measuring trust in HRI [55.2480439325792]
  Trust in robots is widely believed to be imperative for the adoption of robots into people's daily lives. Researchers have been exploring how people trust robot in different ways. Most roboticists agree that insufficient levels of trust lead to a risk of disengagement.
  arXiv  Detail & Related papers  (2023-11-20T20:52:10Z)
• 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)
• Who to Trust, How and Why: Untangling AI Ethics Principles, Trustworthiness and Trust [0.0]
  We argue for the need to distinguish these concepts more clearly. We discuss that trust in AI involves not only reliance on the system itself, but also trust in the developers of the AI system.
  arXiv  Detail & Related papers  (2023-09-19T05:00:34Z)
• Designing for Responsible Trust in AI Systems: A Communication Perspective [56.80107647520364]
  We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH. We highlight transparency and interaction as AI systems' affordances that present a wide range of trustworthiness cues to users. We propose a checklist of requirements to help technology creators identify appropriate cues to use.
  arXiv  Detail & Related papers  (2022-04-29T00:14:33Z)
• 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)
• Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities [94.65749466106664]
  We introduce the concept of trust matrix, a novel trust quantification strategy. A trust matrix defines the expected question-answer trust for a given actor-oracle answer scenario. We further extend the concept of trust densities with the notion of conditional trust densities.
  arXiv  Detail & Related papers  (2020-09-30T14:33:43Z)
• How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks [94.65749466106664]
  We conduct a thought experiment and explore two key questions about trust in relation to confidence. We introduce a suite of metrics for assessing the overall trustworthiness of deep neural networks based on their behaviour when answering a set of questions. The proposed metrics are by no means perfect, but the hope is to push the conversation towards better metrics.
  arXiv  Detail & Related papers  (2020-09-12T17:37:36Z)

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.