Evaluating If Trust and Personal Information Privacy Concerns Are Barriers to Using Health Insurance That Explicitly Utilizes AI
Open accessAbstract
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.
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Differential Privacy introduces controlled noise to data sets to obscure individual identities, permitting the training of AI models on population-representative data without compromising individual privacy.
“Lastly, Differential Privacy (DP) introduces controlled noise to data sets to obscure individual identities, permitting the training of AI models on population-representative data without compromising individual privacy [10].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [10]
The complexity of AI algorithms often makes it challenging to track patient data utilization within these systems, leading to potential misuse or a lack of transparency.
“The complexity of AI algorithms often makes it challenging to track patient data utilization within these systems [9,10], leading to potential misuse or a lack of transparency.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [10]
Differential Privacy's capability to prevent data leakage must be carefully weighed against the potential diminution in prediction accuracy and data utility.
“Its capability to prevent data leakage must be carefully weighed against the potential diminution in prediction accuracy and data utility.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [10]
Differential Privacy presents a robust framework for preserving individual privacy in healthcare AI applications, particularly in distributed data processing contexts.
“DP presents a robust framework for preserving individual privacy in healthcare AI applications, particularly in distributed data processing contexts.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [10]
Risks of reidentification in anonymized data require enhanced privacy and security measures to protect patient data against computational strategies capable of reidentifying individuals.
“Moreover, the risks of reidentification in anonymized data require enhanced privacy and security measures to protect patient data against computational strategies capable of reidentifying individuals.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [10]
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Self-citationRetrieving Under Uncertainty: Towards a Chatbot Uncertainty Taxonomy (CUT) for Information Retrieval
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- Authors
- Zarifis, A., Kawalek, P., & Azadegan, A.
- Affiliations
- Loughborough University (GB)
- University of Reading (GB)
- Corresponding author
- Zarifis, Alex
- Venue
- Journal of Internet Commerce
- Publisher
- Informa UK Limited
- Published
- 2020-10-19
- Volume
- 20
- Issue
- 1
- Pages
- 66-83
- ISSN
- 1533-2861 (ISSN-L)
- 1533-287X
- OpenAlex
- W3094276725
- Cited by
- 89
- References
- 39
- Citations by year
- 2026: 16 · 2025: 27 · 2024: 20 · 2023: 13 · 2022: 10 · 2021: 3
- FWCI
- 6.44
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Internet Commerce (publishedVersion, cc-by-nc-nd)
- ePrints Soton (University of Southampton) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- SSRN Electronic Journal (submittedVersion)
- Loughborough University Research Repository (Loughborough University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (cc-by)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- trust, privacy concerns, health insurance, artificial intelligence, AI adoption barriers, AI visibility, consumer trust in AI, personal information disclosure, health data sharing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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