Authors :
Akazue Maureen; Onovughe Anthonia; Edith Omede; Hampo, John Paul A.C.
Volume/Issue :
Volume 8 - 2023, Issue 1 - January
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3R7HbKp
DOI :
https://doi.org/10.5281/zenodo.7568675
Abstract :
User trust in technology is an essential factor
for the usage of a system or machine. AI enabled
technologies such as virtual digital assistants simplify a
lot of process for humans starting from simple search to
a more complex action like house automation and
completion of some transitions notably Amazon’s Alexa.
Can human actually trust these AI enabled technologies?
Hence, this research applied adaptive boosting ensemble
learning approach to predict users trust in virtual
assistants. A technology trust dataset was obtained from
figshare.com and engineered before training the
adaptive boosting (AdaBoost) algorithm to learn the
trends and pattern. The result of the study showed that
AdaBoost had an accuracy of 94.31% for the testing set.
Keywords :
Machine Learning, Ensemble Model, Predictive Model, Trust, Intelligent Virtual Assistants
User trust in technology is an essential factor
for the usage of a system or machine. AI enabled
technologies such as virtual digital assistants simplify a
lot of process for humans starting from simple search to
a more complex action like house automation and
completion of some transitions notably Amazon’s Alexa.
Can human actually trust these AI enabled technologies?
Hence, this research applied adaptive boosting ensemble
learning approach to predict users trust in virtual
assistants. A technology trust dataset was obtained from
figshare.com and engineered before training the
adaptive boosting (AdaBoost) algorithm to learn the
trends and pattern. The result of the study showed that
AdaBoost had an accuracy of 94.31% for the testing set.
Keywords :
Machine Learning, Ensemble Model, Predictive Model, Trust, Intelligent Virtual Assistants
