The challenge of privacy and security when using technology to track people in times of COVID-19 pandemic

Trustworthy artificial intelligence and the European Union AI act: On the conflation of trustworthiness and acceptability of risk

In its AI Act, the European Union chose to understand trustworthiness of AI in terms of the acceptability of its risks. Based on a narrative systematic literature review on institutional trust and AI in the public sector, this article argues that the EU adopted a simplistic conceptualization of trust and is overselling its regulatory ambition. The paper begins by reconstructing the conflation of "trustworthiness" with "acceptability" in the AI Act. It continues by developing a prescriptive set of variables for reviewing trust research in the context of AI. The paper then uses those variables for a narrative review of prior research on trust and trustworthiness in AI in the public sector. Finally, it relates the findings of the review to the EU's AI policy. Its prospects to successfully engineer citizen's trust are uncertain. There remains a threat of misalignment between levels of actual trust and the trustworthiness of applied AI.

A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs)

Clinical data sharing can facilitate data-driven scientific research, allowing a broader range of questions to be addressed and thereby leading to greater understanding and innovation. However, sharing biomedical data can put sensitive personal information at risk. This is usually addressed by data anonymization, which is a slow and expensive process. An alternative to anonymization is construction of a synthetic dataset that behaves similar to the real clinical data but preserves patient privacy. As part of a collaboration between Novartis and the Oxford Big Data Institute, a synthetic dataset was generated based on images from COSENTYX® (secukinumab) ankylosing spondylitis (AS) clinical studies. An auxiliary classifier Generative Adversarial Network (ac-GAN) was trained to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs), conditioned on the VU location (cervical, thoracic and lumbar). Here, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties along three key metrics: image fidelity, sample diversity and dataset privacy.

Ten GIS-Based Solutions for Managing and Controlling COVID-19 Pandemic Outbreak

The coronavirus (COVID-19) pandemic has caused disastrous results in most countries of the world. It has rapidly spread across the globe with over 156 million cumulative confirmed cases and 3.264 million deaths to date, according to World Health Organization (WHO) Coronavirus Disease (COVID-19) Dashboard. With these huge amounts of causalities in the world, Geographic Information Systems (GIS) as a computer-based analyzer could help governments, experts, medical staff, and citizens to prevent and respond to the incidence. On the other hand, the COVID-19 pandemic involves many unknown parameters where most of them have a spatial dimension. Thus, spatial analysis and GIS could provide appropriate decision-making tools, predictive models, statistical methods, and new technologies for COVID-19 outbreak control, also help the people for avoiding direct contact and preserving social distance. This article aims to review the most promising categories of GIS-based solutions in this domain. We divided the solutions into ten classes including spatio-temporal analysis, SDSS approaches, geo-business, context-aware recommendation systems, participatory GIS and volunteered geographic information (VGI), internet of things (IoT), location-based service (LBS), web mapping, satellite imagery-based analysis, and waste management. The main contribution of this paper is proposing different geospatial guidelines that could provide reliable and useful protocols for COVID-19 outbreak control to minimize causalities, restrict incidence, establish effective urban communication, provide new approaches for business in lockdown situations, telehealth treatment, patient monitoring, adaptive decision making, and visualize trend analysis.

Factors for Online Identity Falsification among Israeli Students in the Era of COVID ‐19

This research investigates the main factors which motivate users to provide falsified details upon website registration, and identifies the types of personal details that are most prone for falsification. In addition, we predict the tendency for identity falsification by examining various factors, such as: privacy concern and socio‐demographic factors. We also relate those issues to the contemporaneous COVID‐19 pandemic and examine its influence on privacy concerns and the willingness to expose personal details. To this end, a user study was carried out among 245 students of the Israeli academia, via a quantitative method using online closed‐ended questionnaires. We found that privacy‐related factors are the most prevalent for identity falsification. In addition, the regression showed the higher the privacy concerns rates, the higher chance for identity falsification. It seems that the COVID‐19 pandemic increased privacy concerns among online users, which may even increase the tendency of the examined behavior.