# Self-assessment in machines boosts human Trust

## TL;DR

Machines that can self-assess improve human trust and team performance in collaborative tasks.

## Contribution

A closed-loop trust calibration system that incorporates machine self-assessment for human-machine collaboration.

## Key findings

- Trained machine self-assessment leads to about 40% improvement in human trust.
- Team performance improves by 5% with self-assessment despite equal machine performance.
- The system is applicable to any semi-autonomous human-machine collaboration.

## Abstract

Low trust in autonomous systems remains a significant barrier to adoption and performance. To effectively increase trust in these systems, machines must perform actions to calibrate human trust based on an accurate assessment of both their capability and human trust in real time. Existing efforts demonstrate the value of trust calibration in improving team performance but overlook the importance of machine self-assessment capabilities in the trust calibration process. In our work, we develop a closed-loop trust calibration system for a human-machine collaboration task to classify images and demonstrate about 40% improvement in human trust and 5% improvement in team performance with trained machine self-assessment compared to the baseline, despite the same machine performance level between them. Our trust calibration system applies to any semi-autonomous application requiring human-machine collaboration.