Clustering Human Trust Dynamics for Customized Real-time Prediction

TL;DR

This paper introduces a clustering-based approach to develop personalized trust prediction models in automation, effectively capturing individual trust dynamics with less data, outperforming general and demographic-based models.

Contribution

The paper presents a novel clustering methodology to create customized trust models that account for individual differences, reducing data requirements and improving prediction accuracy.

Findings

Clustering participants into 'confident' and 'skeptical' groups improves trust prediction.

Customized models outperform general population models.

Models based on trust dynamics enhance trust calibration strategies.

Abstract

Trust calibration is necessary to ensure appropriate user acceptance in advanced automation technologies. A significant challenge to achieve trust calibration is to quantitatively estimate human trust in real-time. Although multiple trust models exist, these models have limited predictive performance partly due to individual differences in trust dynamics. A personalized model for each person can address this issue, but it requires a significant amount of data for each user. We present a methodology to develop customized model by clustering humans based on their trust dynamics. The clustering-based method addresses the individual differences in trust dynamics while requiring significantly less data than personalized model. We show that our clustering-based customized models not only outperform the general model based on entire population, but also outperform simple demographic factor-based customized models. Specifically, we propose that two models based on ``confident'' and ``skeptical'' group of participants, respectively, can represent the trust behavior of the population. The ``confident'' participants, as compared to the ``skeptical'' participants, have higher initial trust levels, lose trust slower when they encounter low reliability operations, and have higher trust levels during trust-repair after the low reliability operations. In summary, clustering-based customized models improve trust prediction performance for further trust calibration considerations.