Start with Privacy by Design in All Big Data Applications

Cavoukian, Ann; Chibba, Michelle

doi:10.1007/978-3-319-53817-4_2

Cited by 19 publications

(15 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It helps discover records of common interest (eg, identities) across systems without transfer of any personally identifiable information. It significantly reduces the risk of unintended info disclosure; 18 …”

Section: Resultsmentioning

confidence: 99%

Ethics of Artificial Intelligence in Medicine and Ophthalmology

Abdullah

Schuman

Shabsigh

et al. 2021

Asia-Pacific Journal of Ophthalmology

View full text Add to dashboard Cite

Background: This review explores the bioethical implementation of artificial intelligence (AI) in medicine and in ophthalmology. AI, which was first introduced in the 1950s, is defined as “the machine simulation of human mental reasoning, decision making, and behavior”. The increased power of computing, expansion of storage capacity, and compilation of medical big data helped the AI implementation surge in medical practice and research. Ophthalmology is a leading medical specialty in applying AI in screening, diagnosis, and treatment. The first Food and Drug Administration approved autonomous diagnostic system served to diagnose and classify diabetic retinopathy. Other ophthalmic conditions such as age-related macular degeneration, glaucoma, retinopathy of prematurity, and congenital cataract, among others, implemented AI too. Purpose: To review the contemporary literature of the bioethical issues of AI in medicine and ophthalmology, classify ethical issues in medical AI, and suggest possible standardizations of ethical frameworks for AI implementation. Methods: Keywords were searched on Google Scholar and PubMed between October 2019 and April 2020. The results were reviewed, cross-referenced, and summarized. A total of 284 references including articles, books, book chapters, and regulatory reports and statements were reviewed, and those that were relevant were cited in the paper. Results: Most sources that studied the use of AI in medicine explored the ethical aspects. Bioethical challenges of AI implementation in medicine were categorized into 6 main categories. These include machine training ethics, machine accuracy ethics, patient-related ethics, physician-related ethics, shared ethics, and roles of regulators. Conclusions: There are multiple stakeholders in the ethical issues surrounding AI in medicine and ophthalmology. Attention to the various aspects of ethics related to AI is important especially with the expanding use of AI. Solutions of ethical problems are envisioned to be multifactorial

show abstract

Section: Resultsmentioning

confidence: 99%

Ethics of Artificial Intelligence in Medicine and Ophthalmology

Abdullah

Schuman

Shabsigh

et al. 2021

Asia-Pacific Journal of Ophthalmology

View full text Add to dashboard Cite

show abstract

“…Although it seems that users do not care much about the perception of privacy in our research setting, this does not mean that chatbot platforms do not need to pay attention to privacy. Due to the popularity of and easy access to AI devices in our daily lives, the active concept of privacy protection referred to as “privacy by design” 104 should be highlighted by device designers. That is, the thought of privacy protection could be initially considered in the design of the product or service.…”

Section: Discussionmentioning

confidence: 99%

Effects of personalization and source expertise on users’ health beliefs and usage intention toward health chatbots: Evidence from an online experiment

Liu

Yan

et al. 2022

DIGITAL HEALTH

View full text Add to dashboard Cite

Objective Based on the heuristic–systematic model (HSM) and health belief model (HBM), this study aims to investigate how personalization and source expertise in responses from a health chatbot influence users’ health belief-related factors (i.e. perceived benefits, self-efficacy and privacy concerns) as well as usage intention. Methods A 2 (personalization vs. non-personalization) × 2 (source expertise vs. non-source expertise) online between-subject experiment was designed. Participants were recruited in China between April and May 2021. Data from 260 valid observations were used for the data analysis. Results Source expertise moderated the effects of personalization on health belief factors. Perceived benefits and self-efficacy mediated the relationship between personalization and usage intention when the source expertise cue was presented. However, the privacy concerns were not influenced by personalization and source expertise and did not significantly affect usage intention toward the health chatbot. Discussion This study verified that in the health chatbot context, source expertise as a heuristic cue may be a necessary condition for effects of the systematic cue (i.e. personalization), which supports the HSM's arguments. By introducing the HBM in the chatbot experiment, this study is expected to provide new insights into the acceptance of healthcare AI consulting services.

show abstract

“…Based on the principles from "privacy by design" by Cavoukian and Chibba (2018), this paper has taken the methodological angle to be user-centric and start with an understanding of how users think. Mental models have been used as a starting point to discuss privacy with a group of user/participants.…”

Section: Methodsmentioning

confidence: 99%

User Perceived Privacy Mental Models of Users’ Perception of App Usage

Sørensen

2018

NBJICT

View full text Add to dashboard Cite

Information privacy is jeopardized almost every time a person uses digital services or applications. The European General Data Protection Regulation, GDPR, recognizes that to empower the users and put focus on privacy, in the future all software (as well as applications) must provide transparency and consent so that the users are protected and are able to manage their privacy in contrast of today. This paper researches the perception of privacy in use of three selected applications; Endomondo, MobilePay and Roskilde Festival apps. In an empirical study, participants have been instructed to draw mental models of different use situations of these applications and to discuss where there needs to be a privacy notification or other to inform the user about the sharing of private data -as announced by the GDPR framework. The work constitutes the first step in a process of understanding the design challenge of the GDPR and for suggesting privacy related design for the interface design with a focus on the user experience.

show abstract

Start with Privacy by Design in All Big Data Applications

Cited by 19 publications

References 11 publications

Ethics of Artificial Intelligence in Medicine and Ophthalmology

Ethics of Artificial Intelligence in Medicine and Ophthalmology

Effects of personalization and source expertise on users’ health beliefs and usage intention toward health chatbots: Evidence from an online experiment

User Perceived Privacy Mental Models of Users’ Perception of App Usage

Contact Info

Product

Resources

About