Clustering Human Trust Dynamics for Customized Real-time Prediction
| Autor: | Liu, Jundi, Akash, Kumar, Misu, Teruhisa, Wu, Xingwei |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA, 2021, pp. 1705-1712 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/ITSC48978.2021.9565016 |
| Popis: | 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. Comment: To be published in 2021 IEEE 24rd International Conference on Intelligent Transportation Systems (ITSC) |
| Databáze: | arXiv |
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