Dynamic Human Trust Modeling of Autonomous Agents With Varying Capability and Strategy

TL;DR

This study models how human trust in autonomous agents varies over time with different capabilities and strategies, revealing the importance of temporal factors and recency bias in trust dynamics.

Contribution

It introduces a time series modeling approach to capture the dynamic nature of human trust in autonomous agents considering performance, strategy, and capability.

Findings

Time series models improve trust prediction accuracy.

Recency bias influences trust evaluation.

Temporal ordering affects trust development.

Abstract

Objective We model the dynamic trust of human subjects in a human-autonomy-teaming screen-based task. Background Trust is an emerging area of study in human-robot collaboration. Many studies have looked at the issue of robot performance as a sole predictor of human trust, but this could underestimate the complexity of the interaction. Method Subjects were paired with autonomous agents to search an on-screen grid to determine the number of outlier objects. In each trial, a different autonomous agent with a preassigned capability used one of three search strategies and then reported the number of outliers it found as a fraction of its capability. Then, the subject reported their total outlier estimate. Human subjects then evaluated statements about the agent's behavior, reliability, and their trust in the agent. Results 80 subjects were recruited. Self-reported trust was modeled using Ordinary Least Squares, but the group that interacted with varying capability agents on a short time order produced a better performing ARIMAX model. Models were cross-validated between groups and found a moderate improvement in the next trial trust prediction. Conclusion A time series modeling approach reveals the effects of temporal ordering of agent performance on estimated trust. Recency bias may affect how subjects weigh the contribution of strategy or capability to trust. Understanding the connections between agent behavior, agent performance, and human trust is crucial to improving human-robot collaborative tasks. Application The modeling approach in this study demonstrates the need to represent autonomous agent characteristics over time to capture changes in human trust.