Human Trust-based Feedback Control: Dynamically varying automation transparency to optimize human-machine interactions

TL;DR

This paper introduces a probabilistic framework to dynamically calibrate human trust in automation by varying transparency, aiming to optimize human-machine interaction performance through adaptive feedback control policies.

Contribution

It presents a novel probabilistic model for trust and workload dynamics and validates adaptive transparency control policies through human-subject experiments.

Findings

Adaptive transparency improves team performance

Control policies outperform non-adaptive systems

Trust calibration enhances human-automation collaboration

Abstract

Human trust in automation plays an essential role in interactions between humans and automation. While a lack of trust can lead to a human's disuse of automation, over-trust can result in a human trusting a faulty autonomous system which could have negative consequences for the human. Therefore, human trust should be calibrated to optimize human-machine interactions with respect to context-specific performance objectives. In this article, we present a probabilistic framework to model and calibrate a human's trust and workload dynamics during his/her interaction with an intelligent decision-aid system. This calibration is achieved by varying the automation's transparency---the amount and utility of information provided to the human. The parameterization of the model is conducted using behavioral data collected through human-subject experiments, and three feedback control policies are experimentally validated and compared against a non-adaptive decision-aid system. The results show that human-automation team performance can be optimized when the transparency is dynamically updated based on the proposed control policy. This framework is a first step toward widespread design and implementation of real-time adaptive automation for use in human-machine interactions.