Search

Supplemental material, sj-docx-1-jre-10.1177_15562646211053538 for The Challenges of Big Data for Research Ethics Committees: A Qualitative Swiss Study by Agata Ferretti, Marcello Ienca, Minerva Rivas Velarde, Samia Hurst and Effy Vayena in Journal of Empirical Research on Human Research Ethics

Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data utility. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely upon bespoke data sharing contracts. The lengthy process and administration induced by these contracts increases the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between 2 novel advanced privacy-enhancing technologies—homomorphic encryption and secure multiparty computation (defined together as multiparty homomorphic encryption). These privacy-enhancing technologies provide a mathematical guarantee of privacy, with multiparty homomorphic encryption providing a performance advantage over separately using homomorphic encryption or secure multiparty computation. We argue multiparty homomorphic encryption fulfills legal requirements for medical data sharing under the European Union’s General Data Protection Regulation which has set a global benchmark for data protection. Specifically, the data processed and shared using multiparty homomorphic encryption can be considered anonymized data. We explain how multiparty homomorphic encryption can reduce the reliance upon customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research while offering additional incentives for health care and research institutes to employ common data interoperability standards., Journal of Medical Internet Research, 23 (2), ISSN:1438-8871

Frontiers in Genetics, 11, ISSN:1664-8021

Background Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice. Methods Taking AI-based clinical decision support systems as a case in point, we adopted a multidisciplinary approach to analyze the relevance of explainability for medical AI from the technological, legal, medical, and patient perspectives. Drawing on the findings of this conceptual analysis, we then conducted an ethical assessment using the “Principles of Biomedical Ethics” by Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice) as an analytical framework to determine the need for explainability in medical AI. Results Each of the domains highlights a different set of core considerations and values that are relevant for understanding the role of explainability in clinical practice. From the technological point of view, explainability has to be considered both in terms how it can be achieved and what is beneficial from a development perspective. When looking at the legal perspective we identified informed consent, certification and approval as medical devices, and liability as core touchpoints for explainability. Both the medical and patient perspectives emphasize the importance of considering the interplay between human actors and medical AI. We conclude that omitting explainability in clinical decision support systems poses a threat to core ethical values in medicine and may have detrimental consequences for individual and public health. Conclusions To ensure that medical AI lives up to its promises, there is a need to sensitize developers, healthcare professionals, and legislators to the challenges and limitations of opaque algorithms in medical AI and to foster multidisciplinary collaboration moving forward., BMC Medical Informatics and Decision Making, 20, ISSN:1472-6947

Governments around the globe have started to develop and deploy digital contact tracing apps to gain control over the spread of the novel coronavirus (Covid-19). The appropriateness and usefulness of these technologies as a containment measure have since sparked political and academic discussions globally. The present paper contributes to this debate through an exploration of how the national daily newspapers in Germany, Austria, and Switzerland reported on the development and adoption of digital contact-tracing apps during early and after stages of the lockdown. These countries were among the first in Europe to develop apps and were critical voices in the debate of decentralized vs. centralized data processing. We conducted thematic analysis on news coverage published between January and May 2020 in high-circulation national daily newspapers (print) from Germany, Austria, and Switzerland. A total of 148 newspaper articles were included in the final analysis. From our analysis emerged six core themes of the development and adoption of digital contact tracing apps: 1) data governance; 2) role of IT giants; 3) scientific rigor; 4) voluntariness; 5) functional efficacy; 6) role of the app. These results shed light on the different facets of discussion regarding digital contact tracing as portrayed in German-speaking media. As news coverage can serve as a proxy for public perception, this study complements emerging survey data on public perceptions of digital contact tracing apps by identifying potential issues of public concern., medRxiv

The aim of this report is to support the Competence Center Citizen Science Zurich (CCCS). Starting out from the point of research oversight in the Swiss context, it gives an overview of existing ethics guidelines in citizen science as promoted by the Citizen Science Association (CSA), the European Citizen Science Association (ECSA) and similar organizations. It highlights the relative lack of attention to ethical governance and processes in current citizen science guidelines and recommends to set up an intra-institutional process to deal with ambiguities in the future.

With increased complexity in various global health challenges comes a need for increased precision and the adoption of more tailored health interventions. Building on precision public health, we propose precision global health (PGH), an approach that leverages life sciences, social sciences, and data sciences, augmented with artificial intelligence (AI), in order to identify transnational problems and deliver targeted and impactful interventions through integrated and participatory approaches. With more than four billion Internet users across the globe and the accelerating power of AI, PGH taps on our current augmented capacity to collect, integrate, analyse and visualise large volumes of data, both non-specific and specific to health. With the support of governments and donors, and together with international and non-governmental organisations, universities and research institutions can generate innovative solutions to improve health and wellbeing of the most vulnerable populations around the world. In line with the Sustainable Development Goals, we propose here a road map for the development and implementation of PGH.

PLoS ONE, 13 (10), ISSN:1932-6203

The implementation of the General Data Protection Regulation (GDPR) raises a series of challenges for scientific research, in particular for research that is dependent on data. This study comprehensively investigates the promises and challenges associated with the implementation of the GDPR in the scientific domain, with a special focus on the impact of the new rights and obligations enshrined in the GDPR on the design and conduct of scientific research. Furthermore, the study examines the adequacy of the GDPR's derogations for scientific research in terms of safeguarding scientific freedom and technological progress. The study also provides policy options that delineate a pathway towards enhancing rather than stifling research, and facilitating privacy-preserving data-driven research under the provisions of the GDPR.

In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies., 42 pages (incl. figures and supplementary information)

L’exposition Les nouveaux états d’être / The New States of Being, 1er volet du projet interdisciplinaire AIship, est présentée au Centre d’exposition de l’Université de Montréal du 27 septembre au 14 décembre 2019., Textes, Jean-Christophe Bélisle-Pipon, Cansu Canca, Laurence Devillers, Pascale Lehoux, Aseman Sabet, Robert Truog, Effy Vayena; commissaire, Aseman Sabet; coordination, Karine Larocque