Sam Muller

Sam Muller is assistant professor of health data governance at the University Medical Center Utrecht's Julius Center for Health Sciences and Primary Care's subdepartment of Bioethics and Health Humanities. He has a background in political science and public administration and organisational science. His research is highly interdisciplinary, intersecting the social sciences (public administration and organisational science, political science, sociology, policy studies), applied ethics (research ethics and bioethics), and science and technology studies (STS).

Sam's work involves a wide range of topics. He has researched the social licence of health data research, involving trustworthiness, public value, and co-creation, as well as the role of accountability to learn from diverse stakeholders about responsible governance of health data research networks. Sam's recent work focuses on the socio-ethically responsible development and governance of health data infrastructures for data and AI-driven research and innovation. He currently works on the diffusion of responsibilities in collaborative health research networks, partnership-based engagement of stakeholder communities involving collaboration and reciprocity, the role of social value in oversight and review, deliberative and participatory governance, responsible surveillance, authorisation of health data usage, return of results, and stakeholder ecosystem mapping.

Sam's PhD thesis focused on responsible governance of Big Data-driven health research, entitled 'Responsible governance for data-intensive health research networks: A learning approach'. His PhD research was part of the work package addressing the governance, ethical, and legal aspects of the Innovative Medicines Initiative's (IMI) BigData@Heart project. By addressing the three challenges of accommodating ethical and legal fragmentation, connecting with stakeholders and society, and managing intractability, heterogeneity and complexity, its aim was to develop an approach enabling responsible governance suitable for Big Data-driven health research taking place in collaborative networks. This research highlighted that learning governance provides a pragmatic and intuitive way to realise governance for data-intensive health research networks, with due regard for the complex social, institutional, and structural context in which governance is situated. Learning governance highlights the self-regulating role of governance for data-intensive health research networks. Moreover, governance is socially sanctioned and depends on a mandate provided by its stakeholders. Lastly, governance fulfills a self-organising function for which incremental learning is essential. Therefore, an experimentalist design strategy, strengthening of collective control, and participatory governance are crucial.