# Trust Considerations for Explainable Robots: A Human Factors Perspective

arXiv: https://arxiv.org/abs/2005.05940 · HTML: https://mlsanity.com/paper/2005.05940/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 5 of 5)

- [Explainable Hierarchical Imitation Learning for Robotic Drink Pouring](https://mlsanity.com/paper/2105.07348/) (2021)
- [Trustworthy AI and Robotics and the Implications for the AEC Industry: A Systematic Literature Review and Future Potentials](https://mlsanity.com/paper/2109.13373/) (2021)
- [Explain yourself! Effects of Explanations in Human-Robot Interaction](https://mlsanity.com/paper/2204.04501/) (2022)
- [BaTCAVe: Trustworthy Explanations for Robot Behaviors](https://mlsanity.com/paper/2409.10733/) (2024)
- [Towards Trustworthy Physical Intelligence: From Theory to Practice Across Life Cycle](https://mlsanity.com/paper/2607.22877/) (2026)

## Abstract (arXiv)

Recent advances in artificial intelligence (AI) and robotics have drawn attention to the need for AI systems and robots to be understandable to human users. The explainable AI (XAI) and explainable robots literature aims to enhance human understanding and human-robot team performance by providing users with necessary information about AI and robot behavior. Simultaneously, the human factors literature has long addressed important considerations that contribute to human performance, including human trust in autonomous systems. In this paper, drawing from the human factors literature, we discuss three important trust-related considerations for the design of explainable robot systems: the bases of trust, trust calibration, and trust specificity. We further detail existing and potential metrics for assessing trust in robotic systems based on explanations provided by explainable robots.