New “classification models” sense how well humans trust intelligent machines they collaborate with, a step toward improving the quality of interactions and teamwork.
The long-term goal of the overall field of research is to design intelligent machines capable of changing their behavior to enhance human trust in them. The new models were developed in research led by assistant professor Neera Jain and associate professor Tahira Reid, in Purdue University’s School of Mechanical Engineering.
“Intelligent machines, and more broadly, intelligent systems are becoming increasingly common in the everyday lives of humans,” Jain said. “As humans are increasingly required to interact with intelligent systems, trust becomes an important factor for synergistic interactions.”
For example, aircraft pilots and industrial workers routinely interact with automated systems. Humans will sometimes override these intelligent machines unnecessarily if they think the system is faltering.
“It is well established that human trust is central to successful interactions between humans and machines,” Reid said.
The researchers have developed two types of “classifier-based empirical trust sensor models,” a step toward improving trust between humans and intelligent machines.
The work aligns with Purdue’s Giant Leaps celebration, acknowledging the university’s global advancements made in AI, algorithms and automation as part of Purdue’s 150th anniversary. This is one of the four themes of the yearlong celebration’s Ideas Festival, designed to showcase Purdue as an intellectual center solving real-world issues.
The models use two techniques that provide data to gauge trust: electroencephalography and galvanic skin response. The first records brainwave patterns, and the second monitors changes in the electrical characteristics of the skin, providing psychophysiological “feature sets” correlated with trust.
Forty-five human subjects donned wireless EEG headsets and wore a device on one hand to measure galvanic skin response.
One of the new models, a “general trust sensor model,” uses the same set of psychophysiological features for all 45 participants. The other model is customized for each human subject, resulting in improved mean accuracy but at the expense of an increase in training time. The two models had a mean accuracy of 71.22 percent, and 78.55 percent, respectively.
It is the first time EEG measurements have been used to gauge trust in real time, or without delay.
“We are using these data in a very new way,” Jain said. “We are looking at it in sort of a continuous stream as opposed to looking at brain waves after a specific trigger or event.”
Findings are detailed in a research paper appearing in a special issue of the Association for Computing Machinery’s Transactions on Interactive Intelligent Systems. The journal’s special issue is titled “Trust and Influence in Intelligent Human-Machine Interaction.” The paper was authored by mechanical engineering graduate student Kumar Akash; former graduate student Wan-Lin Hu, who is now a postdoctoral research associate at Stanford University; Jain and Reid.
“We are interested in using feedback-control principles to design machines that are capable of responding to changes in human trust level in real time to build and manage trust in the human-machine relationship,” Jain said. “In order to do this, we require a sensor for estimating human trust level, again in real-time. The results presented in this paper show that psychophysiological measurements could be used to do this.”