Trust, Safety, and Graceful Failure
Trust as a Design Material
Trust includes attitudes and expectations that influence whether and how a person relies on a robot. Reliance is observable behavior, but it is not identical to trust.
Trust determines whether a user will delegate a task to a robot. It dictates whether they will work alongside it without obsessively monitoring it. Trust determines whether your robot gets used as intended, worked around, or abandoned after a single error.
This distinction matters because designers can measure both reported trust and reliance behavior, then ask whether either is appropriately calibrated to the robot's actual capability.
Trust is shaped by robot performance and attributes, the person, the task, and the environment. Communication is important, but it cannot compensate for poor reliability or unsafe engineering.
References4 selected references · updated Aug 9, 2026
These sources support appropriate reliance, trust calibration, the influence of robot performance, and the effect of errors. Trust and recovery outcomes depend on task, user, system behavior, and context; recovery does not guarantee that trust will exceed its pre-failure level.
- 01Trust in Automation: Designing for Appropriate ReliancePeer-reviewed research
John D. Lee and Katrina A. See · 2004 · Human Factors
Supports: Trust calibration, appropriate reliance, misuse, and disuse of automation.
- 02A Meta-Analysis of Factors Affecting Trust in Human-Robot InteractionPeer-reviewed research
Peter A. Hancock et al. · 2011 · Human Factors
Supports: Human, robot, and environmental factors associated with trust in HRI.
- 03Would You Trust a (Faulty) Robot? Effects of Error, Task Type and Personality on Human-Robot Cooperation and TrustPeer-reviewed research
Maha Salem, Gabriella Lakatos, Farshid Amirabdollahian, and Kerstin Dautenhahn · 2015 · ACM/IEEE International Conference on Human-Robot Interaction
Supports: How robot errors and task type can affect cooperation and trust.
- 04Complacency and Bias in Human Use of Automation: An Attentional IntegrationPeer-reviewed research
Raja Parasuraman and Dietrich H. Manzey · 2010 · Human Factors
Supports: Automation bias, complacency, attention, and monitoring behavior.