Generational differences in trust in digital assistants

Abstract

Human trust in automation has been studied extensively within safety critical domains (military, aviation, process control, etc.) because harmful consequences are associated with the improper calibration of trust in automated systems in these domains (Parasuraman & Riley, 1997). As such, researchers have worked to identify important factors which help humans build trust in such systems (Hoff & Bashir, 2015). With the explosion of AI in consumer technologies, it is becoming equally critical to understand how humans interact with everyday devices. This study investigated how factors that have been identified to impact trust in automation in safety critical domains influence the trust and use of popular digital assistants (Siri, Cortana, Bixby or Google Now). We conducted an online survey with 278 regular users of digital assistants across three generations (GenX, GenY, and GenZ). The results demonstrate that, even after controlling for dispositional factors (i.e., individual characteristics such as age, culture, gender), GenZ exhibited higher trust in digital assistants than GenX. More interestingly, linear regression analyses revealed a different set of predictors of trust for each generation. Results from this survey have implications for the design of digital assistants.

Acceptance and Fear of Artificial Intelligence: associations with personality in a German and a Chinese sample

“…Additionally, a study found that only Agreeableness of the Big Five was positively correlated with trust in digital assistants. However, the association remained significant in a regression model only in individuals from generation Z [40]. Finally, some studies report associations between personality and trust in/agreement with automation.…”

“…Aside from personal characteristics, however, also different characteristics of the AI product and environment can influence differences in attitudes towards AI; this has already been proposed in the specific context of trusting AI [62]. Moreover, a study on trust in digital assistants reports positive associations between trust in these assistants and perceived reliability and system performance factors [40]; for another model about factors of trustworthy AI (or machine learning, specifically), please see, for example, Toreini et al [63] or the review by Glikson and Woolley [64], which additionally takes into account different kinds of AI (robotic, embedded, etc. ).…”