Governance and Algorithms in Care and Welfare

URL study guide

https://studiegids.vu.nl/en/courses/2025-2026/S_GACW3

Course Objective

By the end of this course, studentsknow and are able to evaluate theories from information sciences, sociology, social psychology, governance, health care management, and organisation sciences on Governance and Algorithms in Care and Welfareare able to apply these theories and formulate research questions and hypotheses that contribute to scientific thinking within this fieldare able to explain the societal relevance of a research question using empirical analysis of dataare able to reflect critically on research on Governance and Algorithms in Care and Welfare and to think through strengths and weaknesses of both quantitative and qualitative research methodshave the skills to conduct a literature search: to use feasible and relevant search terms for the domain, evaluate the quality of research questions and theories, and adhere to the guidelines of proper referencinghave attained the skills to describe research questions that are embedded in and emanate from relevant theories on Governance and Algorithms in Care and Welfare so that they are an appropriate starting point for a research proposalare able to value the different disciplinary and cultural input of your group members in relation to the research questions and underlying assumptions of these questions and to benefit from this to enrich the research questions or add original perspectives In addition to these generic learning goals, this course adds content-specific learning goals. Studentsare able to link societal developments, such as population aging or the rise of algorithms as technological innovations, to emerging problems concerning the governance and organisation of care and welfare to promote the wellbeing of individuals and communitiesare able to describe developments in care arrangements from the perspective of the care recipient (in the social network) and/or the care professional (in the care organisation)are able to distill the outcomes of care reforms and their consequences for the wellbeing of individuals and communities; and are able to recognise factors that are vital to the resilience of governance responses to current societal problems

Course Content

This course explores the conditions necessary for the resilient organisation of care and welfare services, with a specific focus on the role and governance of algorithms. These conditions span multiple levels of analysis: macro-societal, meso-organisational, and micro-individual. The course integrates perspectives from sociology, social psychology, and organisation sciences, linking them to broader program themes. Students will engage with a variety of methods
- conceptual, qualitative, quantitative, and computational
- through academic articles and case studies. The course examines how system-level changes, such as government policies, technological innovations, and demographic shifts, shape new forms of care organisation. These may include evolving relationships between public and private care providers, shared decision-making processes in multi-party networks, and the emergence of new professional roles and carer-patient dynamics at the micro level. Governments increasingly rely on algorithms to address social problems, optimise resource allocation, and improve the efficiency and equity of care systems. While algorithms demonstrate great potential in areas like public health analysis, biomedical research, and healthcare service redesign, they also raise significant concerns. Issues such as bias reinforcement, privacy risks, and the delegation of decision-making to automated systems remain contentious. This course provides students with the opportunity to apply knowledge from prior courses to the domain of care and welfare, using these insights to enhance our understanding of key questions such as: How can we reorganise care systems to promote lifelong health rather than merely treating illness? How can responsibilities for care be balanced among individuals, families, and society? How can diverse stakeholders in care networks collaborate effectively? How do we ensure that the adoption of algorithms in care minimises unintended consequences, such as bias or inequality? By addressing these complex and interconnected challenges, students will develop insights that provide crucial input when developing tools to design innovative and equitable solutions for the future of care and welfare systems.

Teaching Methods

In the course, we use different teaching formats: interactive meetings based on a flipped classroom concept, including group presentations, peer review and workgroups which vary in nature (e.g. discussion meetings with students in the other course in P4).

Method of Assessment

Assessment in this course consists of formative and graded written assignments and presentations.

Target Audience

First-year students in the Research Master Social Sciences for a Digital Society