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

arXiv: https://arxiv.org/abs/2602.08707 · HTML: https://mlsanity.com/paper/2602.08707/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 2 of 2)

- [The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering](https://mlsanity.com/paper/2305.16519/) (2023)
- [What is Beautiful is Still Good: The Attractiveness Halo Effect in the era of Beauty Filters](https://mlsanity.com/paper/2407.11981/) (2024)

## Built on by (most cited first, 1 of 1)

- [AI Watchdog: Agent Interfaces for Detecting and Defending Against Manipulative Dark Patterns in AI Conversations](https://mlsanity.com/paper/2608.21841/) (2026)

## Abstract (arXiv)

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.