  The work reported here addresses the capacity of psychophysiological sensors
and measures using Electroencephalogram (EEG) and Galvanic Skin Response (GSR)
to detect levels of trust for humans using AI-supported Human-Machine
Interaction (HMI). Improvements to the analysis of EEG and GSR data may create
models that perform as well, or better than, traditional tools. A challenge to
analyzing the EEG and GSR data is the large amount of training data required
due to a large number of variables in the measurements. Researchers have
routinely used standard machine-learning classifiers like artificial neural
networks (ANN), support vector machines (SVM), and K-nearest neighbors (KNN).
Traditionally, these have provided few insights into which features of the EEG
and GSR data facilitate the more and least accurate predictions - thus making
it harder to improve the HMI and human-machine trust relationship. A key
ingredient to applying trust-sensor research results to practical situations
and monitoring trust in work environments is the understanding of which key
features are contributing to trust and then reducing the amount of data needed
for practical applications. We used the Local Interpretable Model-agnostic
Explanations (LIME) model as a process to reduce the volume of data required to
monitor and enhance trust in HMI systems - a technology that could be valuable
for governmental and public sector applications. Explainable AI can make HMI
systems transparent and promote trust. From customer service in government
agencies and community-level non-profit public service organizations to
national military and cybersecurity institutions, many public sector
organizations are increasingly concerned to have effective and ethical HMI with
services that are trustworthy, unbiased, and free of unintended negative
consequences.
