Data sharing: using blockchain and decentralized data technologies to unlock the potential of artificial intelligence: What can assisted reproduction learn from other areas of medicine?

Personal data sharing has become an important issue in public and private sectors of our society. However, data subjects are perceived to be always unwilling to share their data on security and privacy reasons. They apprehend that those data will be misused at the cost of their privacy jeopardising their human rights. Thus, personal data sharing is closely associated with human right issues. This concern of data subjects has increased manifolds owing to the interference of Artificial Intelligence (AI) since AI can analyse data without human intervention. In this background, this article has taken an attempt to investigate how applications of AI and imposition of regulatory controls with appropriate governance can influence the impact of personal data sharing on the issues of human right abuses.

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Overcoming the Challenges of Collaboratively Adopting Artificial Intelligence in the Public Sector

Social Science Computer Review ◽

10.1177/0894439320979953 ◽

2020 ◽

pp. 089443932097995

Author(s):

Averill Campion ◽

Mila Gasco-Hernandez ◽

Slava Jankin Mikhaylov ◽

Marc Esteve

Keyword(s):

Artificial Intelligence ◽

Data Sharing ◽

Research University ◽

Steady Increase ◽

Organizational Routines ◽

Privacy And Security ◽

Collaborative Project ◽

The Public ◽

Interorganizational Collaborations

Despite the current popularity of artificial intelligence (AI) and a steady increase in publications over time, few studies have investigated AI in public contexts. As a result, assumptions about the drivers, challenges, and impacts of AI in government are far from conclusive. By using a case study that involves a large research university in England and two different county councils in a multiyear collaborative project around AI, we study the challenges that interorganizational collaborations face in adopting AI tools and implementing organizational routines to address them. Our findings reveal the most important challenges facing such collaborations: a resistance to sharing data due to privacy and security concerns, insufficient understanding of the required and available data, a lack of alignment between project interests and expectations around data sharing, and a lack of engagement across organizational hierarchy. Organizational routines capable of overcoming such challenges include working on-site, presenting the benefits of data sharing, reframing problems, designating joint appointments and boundary spanners, and connecting participants in the collaboration at all levels around project design and purpose.

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On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management

Healthcare ◽

10.3390/healthcare8010046 ◽

2020 ◽

Vol 8 (1) ◽

pp. 46 ◽

Cited By ~ 70

Author(s):

Zaheer Allam ◽

David S. Jones

Keyword(s):

Artificial Intelligence ◽

Data Sharing ◽

Urban Health ◽

Health Monitoring ◽

Smart City ◽

Political Influence ◽

Health Data ◽

The Other ◽

City Network ◽

Global Understanding

As the Coronavirus (COVID-19) expands its impact from China, expanding its catchment into surrounding regions and other countries, increased national and international measures are being taken to contain the outbreak. The placing of entire cities in ‘lockdown’ directly affects urban economies on a multi-lateral level, including from social and economic standpoints. This is being emphasised as the outbreak gains ground in other countries, leading towards a global health emergency, and as global collaboration is sought in numerous quarters. However, while effective protocols in regard to the sharing of health data is emphasised, urban data, on the other hand, specifically relating to urban health and safe city concepts, is still viewed from a nationalist perspective as solely benefiting a nation’s economy and its economic and political influence. This perspective paper, written one month after detection and during the outbreak, surveys the virus outbreak from an urban standpoint and advances how smart city networks should work towards enhancing standardization protocols for increased data sharing in the event of outbreaks or disasters, leading to better global understanding and management of the same.

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Data for sale: trust, confidence and sharing health data with commercial companies

Journal of Medical Ethics ◽

10.1136/medethics-2021-107464 ◽

2021 ◽

pp. medethics-2021-107464

Author(s):

Mackenzie Graham

Keyword(s):

Artificial Intelligence ◽

Data Sharing ◽

Health Data ◽

Computing Power ◽

Health Institutions

Powered by ‘big health data’ and enormous gains in computing power, artificial intelligence and related technologies are already changing the healthcare landscape. Harnessing the potential of these technologies will necessitate partnerships between health institutions and commercial companies, particularly as it relates to sharing health data. The need for commercial companies to be trustworthy users of data has been argued to be critical to the success of this endeavour. I argue that this approach is mistaken. Our interactions with commercial companies need not, and should not, be based on trust. Rather, they should be based on confidence. I begin by elucidating the differences between trust, reliability, and confidence, and argue that trust is not the appropriate attitude to adopt when it comes to sharing data with commercial companies. I argue that what we really should want is confidence in a system of data sharing. I then provide an outline of what a confidence-worthy system of data sharing with commercial companies might look like, and conclude with some remarks about the role of trust within this system.

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The NOMAD laboratory: from data sharing to artificial intelligence

Journal of Physics Materials ◽

10.1088/2515-7639/ab13bb ◽

2019 ◽

Vol 2 (3) ◽

pp. 036001 ◽

Cited By ~ 25

Author(s):

Claudia Draxl ◽

Matthias Scheffler

Keyword(s):

Artificial Intelligence ◽

Data Sharing

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Fairness and Ethics in Artificial Intelligence-Based Medical Imagining

10.4018/978-1-7998-7888-9.ch004 ◽

2022 ◽

pp. 71-85

Author(s):

Satvik Tripathi ◽

Thomas Heinrich Musiolik

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Patient Care ◽

Data Sharing ◽

Ethical Issues ◽

Medical Systems ◽

Healthcare Facilities ◽

The World ◽

Potential Applications ◽

Medical Imagining

Artificial intelligence has a huge array of current and potential applications in healthcare and medicine. Ethical issues arising due to algorithmic biases are one of the greatest challenges faced in the generalizability of AI models today. The authors address safety and regulatory barriers that impede data sharing in medicine as well as potential changes to existing techniques and frameworks that might allow ethical data sharing for machine learning. With these developments in view, they also present different algorithmic models that are being used to develop machine learning-based medical systems that will potentially evolve to be free of the sample, annotator, and temporal bias. These AI-based medical imaging models will then be completely implemented in healthcare facilities and institutions all around the world, even in the remotest areas, making diagnosis and patient care both cheaper and freely accessible.

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Data Mining Under the Directive on Copyright and Related Rights in the Digital Single Market: Are European Database Protection Rules Still Threatening the Development of Artificial Intelligence?

GRUR International ◽

10.1093/grurint/ikaa046 ◽

2020 ◽

Vol 69 (5) ◽

pp. 457-473 ◽

Cited By ~ 1

Author(s):

Romain Meys

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

European Union ◽

Data Sharing ◽

Personal Data ◽

The European Union ◽

European Database ◽

Data User ◽

Database Protection ◽

Start Ups

Abstract This paper explores how the existing European rules on the legal and contractual protection of databases limit the re-use of non-personal data by start-ups and SMEs for the purpose of developing artificial intelligence in the European Union. This analysis aims to determine whether the recent initiatives on data mining and data sharing are adequate to ensure an appropriate level of data re-usability for that purpose. In turn, this paper argues that additional reforms are needed to establish a more balanced European framework on the legal and contractual protection of databases. Therefore, it contemplates the introduction of data user rights, which would facilitate the access and re-use of non-personal data by the enterprises in question.

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Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence

Neurology ◽

10.1212/wnl.0000000000012884 ◽

2021 ◽

pp. 10.1212/WNL.0000000000012884

Author(s):

Hugo Vrenken ◽

Mark Jenkinson ◽

Dzung Pham ◽

Charles R.G. Guttmann ◽

Deborah Pareto ◽

...

Keyword(s):

Artificial Intelligence ◽

Image Analysis ◽

Data Sharing ◽

Large Scale ◽

Personal Data ◽

Human Observer ◽

Imaging Data ◽

Large Scale Data ◽

Scale Data

Multiple sclerosis (MS) patients have heterogeneous clinical presentations, symptoms and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data-sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using magnetic resonance imaging (MRI).First, development of validated MS-specific image analysis methods can be boosted by verified reference, test and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy or functional network changes) to large multi-domain datasets (imaging, cognition, clinical disability, genetics, etc.).After reviewing data-sharing and artificial intelligence, this paper highlights three areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging and the understanding of MS.

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The Biometric Assemblage: Surveillance, Experimentation, Profit, and the Measuring of Refugee Bodies

Television & New Media ◽

10.1177/1527476419857682 ◽

2019 ◽

Vol 20 (6) ◽

pp. 581-599 ◽

Cited By ~ 10

Author(s):

Mirca Madianou

Keyword(s):

Artificial Intelligence ◽

United Nations ◽

Data Sharing ◽

United Nations High Commissioner ◽

Global Context ◽

Technological Convergence ◽

High Commissioner ◽

The World ◽

Vulnerable People ◽

Central Population

Biometric technologies are routinely used in the response to refugee crises with the United Nations High Commissioner for Refugees (UNHCR) aiming to have all refugee data from across the world in a central population registry by the end of 2019. The article analyzes biometrics, artificial intelligence (AI), and blockchain as part of a technological assemblage, which I term the biometric assemblage. The article identifies five intersecting logics that explain wider transformations within the humanitarian sector and in turn shape the biometric assemblage. The acceleration of the rate of biometric registrations in the humanitarian sector between 2002 and 2019 reveals serious concerns regarding bias, data safeguards, data-sharing practices with states and commercial companies, experimentation with untested technologies among vulnerable people, and, finally, ethics. Technological convergence amplifies risks associated with each constituent technology of the biometric assemblage. The article finally argues that the biometric assemblage accentuates asymmetries between refugees and humanitarian agencies and ultimately entrenches inequalities in a global context.

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