Healthcare Ethics, Legal and Social Impact
What makes something data? What makes something a scientific fact? Who/what curates and shapes data, especially in healthcare, and how does that impact patients, their health and the delivery of care?
This is a course that I wanted to design and teach for a long time, and finally got the chance to do so as part of the new master’s program in Data-driven Health. The course was born out of the desire to do something to educate students on a few incredibly challenging problems: designing the technological infrastructure of the future, assessing the role of institutions, cultures and norms in shaping what we perceive to be data and knowledge, and analyzing how science, technology and politics shape ideas about race and gender.
A huge part of the motivation for the course is also to elevate the discourse around biases in data and AI. To center the course around the intersection of equity and justice in AI, to acknowledge the inherent lack of neutrality in computing and analayze the root causes, from a historical, decolonial point of view.
The course sits squarely at the intersection between Science and Technology Studies (STS) and Critical Data Studies (CDS), both highly inter-disciplinary fields which uses theories from anthropology, sociology, philosophy, and history to understand the interconnections between science, technology, and society.
I have outlined the structure of the course, and listed the material we used in the course. There are undoubtedly more rigorous, detailed courses on similar themes elsewhere, this is meant for engineering students who have never had to engage with these themes or material of this nature. The reading material is also a very small selection of the work done by scholars and activists in articulating the origins, development, and hegemony of the modern tech ecosystem. My hope is that this these will act as inspiration (as it did for our students), and will form a place to situate oneself within and start exploring from.
If you have comments or suggestions for material, please reach out. If you want to use this in your own course, please send me a note so I know.
Mikaela Hellstrand (one of my PhD students), was an invaluable collaborator in designing and running this course. She helped select a lot of the material, particularly Seminars 1 and 4, and helped sharpen and clarify the goals and outcomes for this course.
Syllabus
Seminar 1: Primer
A basic primer on terminology and frameworks, to act as a lens to the intersecting fields of ethics, law, epistemology and so on.
boyd, danah, & Crawford, K. (2012). CRITICAL QUESTIONS FOR BIG DATA: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679.
Högberg, C. (2025). “This ground truth is muddy anyway”: Ground Truth Data Assemblages for Medical AI Development.
Stevens M (2021) Dreaming with data: Assembling responsible knowledge practices in data-driven healthcare. PhD Thesis. Erasmus University Rotterdam, the Netherlands. Introduction: Knowing with data-driven technologies (pp.7-24).
Berg, M., & Goorman, E. (1999). The contextual nature of medical information. International Journal of Medical Informatics, 56(1), 51–60.
Seminar 2: Law
Assoc. Prof. Stanley Greenstein gave a lecture on legislation, and how that operates in this space.
Seminar 3: Health Ethics
Dr. Barbro Fröding gave a lecture on ethics, focusing on principalism, and ethical principles.
Seminar 4: Data-Knowledge-Power
This seminar digs deeper into the interplay between the concepts of data, knowledge and power. Departing from a perspective which views data as a sociotechnical phenomenon, we explore how data connects to power and politics, how data-driven ways of working affects everyday health care contexts, what happens when the logic of care meet the logic of data and the role of the body in knowledge production and how this relates to data
Carboni, C., Wehrens, R., van der Veen, R., & de Bont, A. (2025). From Attention to Attunement: Data-Driven Efficiency and Embodied Care in the Intensive Care Unit. Science, Technology, & Human Values, 0(0). Links to an external site.
Ebeling, M. F. (2023). Big data in healthcare. In Encyclopedia of Health Research in the Social Sciences (pp. 7-11). Edward Elgar Publishing.
Gallistl, V., & Von Laufenberg, R. (2024). Caring for data in later life–the datafication of ageing as a matter of care. Information, Communication & Society, 27(4), 774-789.
Hoeyer, K. (2023). Data paradoxes: The politics of intensified data sourcing in contemporary healthcare. MIT Press. Introduction chapter (pp. 1-28).
Mejias, U. A. & Couldry, N. (2019). Datafication. Internet Policy Review, 8(4).
Lupton, D. (2018). How do data come to matter? Living and becoming with personal data. Big Data & Society, 5(2).
Seminar 5: Decolonizing Data
Understand the dominant sources of power, and how they intersect with and shape technology, and mediate what we understand as knowledge and data.
Boaventura de Sousa Santos: Beyond Abyssal Thinking: From Global Lines to Ecologies of Knowledges
Syed Mustafa Ali, A brief introduction to decolonial computing.
Couldry, N., & Mejias, U. A. (2023). The decolonial turn in data and technology research: What is at stake and where is it heading? Information, Communication & Society, 26(4), 786–802.
Grosfoguel, R. (2011). Decolonizing Post-Colonial Studies and Paradigms of Political-Economy: Transmodernity, Decolonial Thinking, and Global Coloniality TRANSMODERNITY: Journal of Peripheral Cultural Production of the Luso-Hispanic World, 1(1).
Seminar 6: Networks, and Surveillance Capitalism
A deeper dive into how power is accumulated and leveraged in modern technology, companies and institutions.
Manuel Castells, A Network Theory of Power
Yin Liang, Jeremy Aroles, Bernd Brandl: Charting platform capitalism: Definitions, concepts and ideologies
Kean Birch & D. T. Cochrane: Big Tech: Four Emerging Forms of Digital Rentiership
Shoshana Zuboff: Big other: Surveillance Capitalism and the Prospects of an Information Civilization
Nancy Fraser: Abnormal Justice
Jonathan Cinnamon: Social Justice in Surveillance Capitalism
Seminar 7: Data Feminism
A lecture by Assoc. Prof. Amir H Payberah on Data Feminism. More material on this can be found on his site.
Seminar 8: FAIR
More on foundations, idealogies, and how proposed solutions fall short.
Wilkinson et. al, The FAIR Guiding Principles for scientific data management and stewardship
Bender et. al, On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
Gebru and Torres: The TESCREAL bundle: Eugenics and the promise of utopia through artificial general intelligence
Olteanu et. al: Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor