Surveillance, security, and AI as technological acceptance

Empowering individuals to adopt artificial intelligence for health information seeking: A latent profile analysis among users in Hong Kong

RATIONALES

Using AI for health information seeking is a novel behavior, and as such, developing effective communication strategies to optimize AI adoption in this area presents challenges. To lay the groundwork, research is needed to map out users' behavioral underpinnings regarding AI use, as understanding users' needs, concerns and perspectives could inform the design of targeted and effective communication strategies in this context.

OBJECTIVE

Guided by the planned risk information seeking model and the comprehensive model of information seeking, our study examines how socio-psychological factors (i.e., attitudes, perceived descriptive and injunctive norms, self-efficacy, technological anxiety) and factors related to information carriers (i.e., trust in and perceived accuracy of AI), shape users' latent profiles. In addition, we explore how individual differences in demographic attributes and anthropocentrism predict membership in these user profiles.

METHODS

We conducted a quota-sampled survey with 1051 AI-experienced users in Hong Kong. Latent profile analysis was used to examine users' profile patterns. The hierarchical multiple logistic regression was employed to examine how individual differences predict membership in these user profiles.

RESULTS

The latent profile analysis revealed five heterogeneous profiles, which we labeled "Discreet Approachers," "Casual Investigators," "Apprehensive Moderates," "Apathetic Bystanders," and "Anxious Explorers." Each profile was associated with specific predictors related to individual differences in demographic attributes and/or aspects of anthropocentrism.

CONCLUSION

The findings advance theoretical understandings of using AI for health information seeking, provide theory-driven strategies to empower users to make well-informed decisions, and offer insights to optimize the adoption of AI technology.

"Ethically contentious aspects of artificial intelligence surveillance: a social science perspective"

Artificial intelligence and its societal and ethical implications are complicated and conflictingly interpreted. Surveillance is one of the most ethically challenging concepts in AI. Within the domain of artificial intelligence, this study conducts a topic modeling analysis of scientific research on the concept of surveillance. Seven significant scholarly topics that receive significant attention from the scientific community were discovered throughout our research. These topics demonstrate how ambiguous the lines between dichotomous forms of surveillance are: public health surveillance versus state surveillance; transportation surveillance versus national security surveillance; peace surveillance versus military surveillance; disease surveillance versus surveillance capitalism; urban surveillance versus citizen ubiquitous surveillance; computational surveillance versus fakeness surveillance; and data surveillance versus invasive surveillance. This study adds to the body of knowledge on AI ethics by focusing on controversial aspects of AI surveillance. In practice, it will serve as a guideline for policymakers and technology companies to focus more on the intended and unintended consequences of various forms of AI surveillance in society.