New Models Sense Human Trust in Smart Machines

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To validate their method, 581 online participants were asked to operate a driving simulation in which a computer identified road obstacles. In some scenarios, the computer correctly identified obstacles 100 percent of the time, whereas in other scenarios the computer incorrectly identified the obstacles 50 percent of the time.

“So, in some cases it would tell you there is an obstacle, so you hit the brakes and avoid an accident, but in other cases it would incorrectly tell you an obstacle exists when there was none, so you hit the breaks for no reason,” Reid said.

The testing allowed the researchers to identify psychophysiological features that are correlated to human trust in intelligent systems, and to build a trust sensor model accordingly. “We hypothesized that the trust level would be high in reliable trials and be low in faulty trials, and we validated this hypothesis using responses collected from 581 online participants,” she said.

The results validated that the method effectively induced trust and distrust in the intelligent machine.

“In order to estimate trust in real time, we require the ability to continuously extract and evaluate key psychophysiological measurements,” Jain said. “This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor.”

The EEG headset records signals over nine channels, each channel picking up different parts of the brain.

“Everyone’s brainwaves are different, so you need to make sure you are building a classifier that works for all humans.”

For autonomous systems, human trust can be classified into three categories: dispositional, situational, and learned.

Dispositional trust refers to the component of trust that is dependent on demographics such as gender and culture, which carry potential biases.

“We know there are probably nuanced differences that should be taken into consideration,” Reid said. “Women trust differently than men, for example, and trust also may be affected by differences in age and nationality.”

Situational trust may be affected by a task’s level of risk or difficulty, while learned is based on the human’s past experience with autonomous systems.

The models they developed are called classification algorithms.

 “The idea is to be able to use these models to classify when someone is likely feeling trusting versus likely feeling distrusting,” she said.

Jain and Reid have also investigated dispositional trust to account for gender and cultural differences, as well as dynamic models able to predict how trust will change in the future based on the data.

The research is funded by the National Science Foundation. The researchers have published several papers since the work began in 2015.

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