A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

Abstract

Today, intelligent machines interact and collaborate with humans in a way that demands a greater level of trust between human and machine. A first step towards building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real-time. In this paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust sensor model based on the general feature set (i.e., a "general trust sensor model"). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.

Real-Time Estimation of Drivers’ Trust in Automated Driving Systems

“…In addition to developing trust-related dynamic models, researchers have tried to use different psychophysiological signals to estimate trust. For instance, extending Hu's work [17], Akash et al [1] proposed schemes for controlling users' trust levels, applying electroencephalography and galvanic skin response measurements for trust estimation. However, psychophysiology-based methods suffer from at least two drawbacks.…”

“…However, there is still much to learn about real-time trust estimation techniques as the current approaches have various limitations. Current approaches fail to provide trust measurements in scales traditionally used for trust in automation [1], or require prohibitive sophisticated sensing and perception methods [1,26]. These sophisticated methods include the processing of psychophysiological signals (e.g.…”

“…The framework is based on observable measures of drivers' behaviors and trust dynamic models. Although different trust estimation approaches have been previously reported in the literature [1,26], our method is simpler to implement. Those previous approaches represented trust as conditional probabilities.…”

Toward Adaptive Trust Calibration for Level 2 Driving Automation

“…Researchers have proposed to develop paradigms that anticipate human interaction behaviors-such as trust in automation-and influence humans to make optimal choices about automation use [1,17,29,38]. Pre-requisites for such an approach involve the capability to quantitatively predict human behavior and an algorithm for determining the optimal intervention to influence human behavior.…”

“…In this setting, we believe trust calibration is especially important because of two reasons: 1) the system reliability (at least user's perceived system reliability) is affected by the scene complexity 2) user trust to the system might change frequently depending on system reliability and traffic condition given that most users are not trained experts. We develop a probabilistic model of the user trust and workload dynamics using human 1 Following those previous studies we define transparency as "the descriptive quality of an interface pertaining to its abilities to afford an operator's comprehension about an intelligent agent's intent, performance, future plans, and reasoning process" [10]. subject data collected using a driving simulator for urban driving scenes.…”