Evaluating If Trust and Personal Information Privacy Concerns Are Barriers to Using Health Insurance That Explicitly Utilizes AI

Abstract

Trust and privacy have emerged as significant concerns in online transactions. Sharing information on health is especially sensitive but it is necessary for purchasing and utilizing health insurance. Evidence shows that consumers are increasingly comfortable with technology in place of humans, but the expanding use of AI potentially changes this. This research explores whether trust and privacy concern are barriers to the adoption of AI in health insurance. Two scenarios are compared: The first scenario has limited AI that is not in the interface and its presence is not explicitly revealed to the consumer. In the second scenario there is an AI interface and AI evaluation, and this is explicitly revealed to the consumer. The two scenarios were modeled and compared using SEM PLS-MGA. The findings show that trust is significantly lower in the second scenario where AI is visible. Privacy concerns are higher with AI but the difference is not statistically significant within the model.

AI invading the workplace: negative emotions towards the organizational use of personal virtual assistants

“…(P31) This constitutes another boundary for PVA design and implementation. Still, it cannot be viewed in isolation from privacy and trust, considering Zarifis et al (2021) finding that trust is lower and privacy concerns are higher when the user can clearly recognize AI.…”

How to explain AI systems to end users: a systematic literature review and research agenda

“…Thus, practitioners are encouraged to take promising design recommendations and adapt them into practice, but measure their effects on end users. For example, it is worthy to investigate whether AI system communication has the potential to alleviate trust issues that end users face with AI systems (Zarifis et al, 2020) or technostress (Tarafdar et al, 2020). Continuous measurement and feedback are important, as each system and use case are unique.…”

“…For example, ML approaches have been used to combat the COVID-19 pandemic through patient outcome prediction, risk assessment and predicting the disease spreading (Dogan et al, 2021), and are an integral component of recommendation systems that curate social media feeds and e-commerce (Batmaz et al, 2019). To reinforce public trust in AI-driven and AI-supported decision making, and to mitigate prejudices (Zarifis et al, 2020) it is pivotal to ensure the explainability of AI-made decisions to the end users of these systems (European Commission, 2020).…”

The dark sides of AI

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