mlsanityData to 7 Oct 2026

Trusting Your AI Agent Emotionally and Cognitively: Development and Validation of a Semantic Differential Scale for AI Trust

arXiv 2408.05354 ↗First posted 25 Jul 2024cs.HC cs.AIcited by 2

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

Trust is not just a cognitive issue but also an emotional one, yet the research in human-AI interactions has primarily focused on the cognitive route of trust development. Recent work has highlighted the importance of studying affective trust towards AI, especially in the context of emerging human-like LLMs-powered conversational agents. However, there is a lack of validated and generalizable measures for the two-dimensional construct of trust in AI agents. To address this gap, we developed and validated a set of 27-item semantic differential scales for affective and cognitive trust through a scenario-based survey study. We then further validated and applied the scale through an experiment study. 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. Our study methodology and findings also provide insights into the capability of the state-of-art LLMs to foster trust through different routes.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 6 of its 104 references that are in the map. First posted on arXiv 25 Jul 2024.

Builds on

6 of 104 references.

References · 6

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  3. 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 166
  4. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
  5. 2022Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methodscited by 13
  6. 2021Toward Affective XAI: Facial Affect Analysis for Understanding Explainable Human-AI Interactionscited by 2

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Measuring Agents in Productioncited by 35
  2. 2025The Trust in AI-Generated Health Advice (TAIGHA) Scale and Short Version (TAIGHA-S): Development and Validation Studycited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Evaluating LLM Alignment With Human Trust Models
  2. 2025A closer look at how large language models trust humans: patterns and biases
  3. 2025When Trust Collides: Decoding Human-LLM Cooperation Dynamics through the Prisoner's Dilemma
  4. 2026Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration
  5. 2026From Role to Person: Trust Calibration Challenges in Twin Agents
  6. 2025Can LLMs Reason About Trust?: A Pilot Study
  7. 2026Does Capability Transfer to Subjective Behavior -- and Would Our Instruments Tell Us? A Self-Evolving, Trust-by-Construction Evaluation Paradigm
  8. 2024Can Large Language Model Agents Simulate Human Trust Behavior?