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Related Experiment Video

Updated: Jun 12, 2025

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

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.

Keywords:

autonomous systems human-machine teaming machine self-assessment trust calibration trust in AI

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Last Updated: Jun 12, 2025

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

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Published on: January 7, 2019

Observing the Transformation of Bodily Self-consciousness in the Squeeze-machine Experiment

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Published on: March 8, 2019

One Dimensional Turing-Like Handshake Test for Motor Intelligence

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Published on: December 15, 2010

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.