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Self-assessment in machines boosts human Trust.
Dana Warmsley1, Krishna Choudhary1, Jocelyn Rego1
1Intelligent Systems Center, HRL Laboratories, Malibu, CA, United States.
Frontiers in Robotics and AI
|June 10, 2025
Summary
Machines can improve human trust and team performance by assessing their own capabilities in real-time. This trust calibration system enhances human-machine collaboration, boosting adoption of autonomous systems.
Area of Science:
- Human-Computer Interaction
- Artificial Intelligence
- Robotics
Background:
- Low trust in autonomous systems hinders their widespread adoption and effectiveness.
- Current trust calibration methods often neglect the machine's self-assessment capabilities.
- Effective human-machine collaboration requires synchronized trust levels.
Purpose of the Study:
- To develop and evaluate a closed-loop trust calibration system for human-machine collaboration.
- To investigate the impact of machine self-assessment on human trust and team performance.
- To demonstrate the system's applicability in semi-autonomous image classification tasks.
Main Methods:
- Implemented a closed-loop system where machines assess their capabilities and human trust in real-time.
- Designed a human-machine collaboration task for image classification.
- Compared a trained machine self-assessment approach against a baseline without it.
Main Results:
- Achieved approximately 40% improvement in human trust levels.
- Observed a 5% increase in overall team performance.
- Demonstrated these gains were achieved with identical machine performance levels between conditions.
Conclusions:
- Machine self-assessment is a critical component for effective trust calibration in human-machine systems.
- The developed trust calibration system significantly enhances both trust and performance in collaborative tasks.
- The system is adaptable to various semi-autonomous applications requiring human-machine interaction.
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