Graduate School of health and Medical Sciences

Critical Perspectives on Artificial Intelligence and Automation
Provider: Faculty of Health and Medical Sciences

Activity no.: 3900-24-00-00	There are no available seats
	Enrollment deadline: 04/11/2024

Date and time	02.12.2024, at: 09:00 - 05.12.2024, at: 16:00
Regular seats	20
Course fee	6,960.00 kr.
Lecturers	Tibor V Varga
ECTS credits	2.80

Contact person	Kathe Jensen E-mail address: kje@sund.ku.dk

Enrolment Handling/Course Organiser	PhD administration E-mail address: phdkursus@sund.ku.dk

Aim and content This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrolment deadline, available seats will be allocated to the waiting list. The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee. Learning objectives A student who has met the objectives of the course will be able to: 1. Utilise conceptual frameworks developed in Science and Technology Studies, critical theory, and the nascent field of critical algorithmic studies to understand ‘artificial intelligence’ not only as a kind of computing, but also as a “form of knowledge production, a paradigm for social organization and a political project” (Resisting AI, 2023). 2. Trace the historical development of the field of ‘artificial intelligence’ and ‘machine learning’ and its ideological underpinnings from its roots in eugenics and race science in the 19th century to the modern-day nexus of ideologies at the intersection of transhumanism, longtermism, and Effective Altruism. 3. Think critically about the purported benefits of automation, stay cognizant of its hidden costs, and holistically assess the tradeoff between the two when engaging with ‘artificial intelligence’ in own research or daily lives. 4. Substitute the catch-all marketing term ‘artificial intelligence’ with language more accurately describing particular kinds of automation. 5. Reject the idea of technological progress as inevitable and deterministic and assume an active role in deciding on the kinds of future towards which we may want to strive. Content Ubiquity of ‘artificial intelligence’, from risk assessment models to generative ‘AI’, has seen explosive growth ever since the deep learning boom that started in the early 2010s. New architectures, tools, and applications are constantly being presented to the general public, academics, and businesses as the next big thing that will democratise education, research, and healthcare, help address every societal crisis, and generally bring about a prosperous future, in a flurry of competing offerings so high-paced that we rarely have time to pause and ask critical – and critically important – questions. Even though a growing number of voices are raising concerns about the broader societal and ecological implications of the bundle of technologies collectively referred to as ‘AI’, these voices are often drowned out by the narrative pushed by the companies at the helm of the current ‘AI’ boom, contributing to the growing disconnect between perspectives on technology developed in the humanities and those held by practitioners in the fields of machine learning and data science. This course makes a step towards bridging this gape by providing an introduction to critical algorithmic studies, science and technology studies, and critical theory and enabling students to apply lenses developed in these fields to more holistically think about ‘artificial intelligence’ and the consequences of deployment of automation. Rather than accepting the narrow techno-optimist view of ‘AI’, which encourages us to treat it as a neutral tool and to ignore historical and political context in which technology is developed and deployed, the students will be equipped to situate it within a rich – and often murky – context that should shape our engagement with automation tools in research and beyond. Participants PhD students who have interest in thinking about ‘artificial intelligence’ more holistically. Students of all levels of hands-on experience with all forms of ‘AI’ are welcome to join the course. The maximum number of participants is 20. In case the course is forced into an online setting (e.g., due to COVID-19), the lectures can be attended by others, but the practical exercises are always limited to the max. 20 signed up participants. Relevance to graduate programmes The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH: The course will be relevant for a large number of graduate programmes due to the deepening penetration of various forms of ‘artificial intelligence’ into many stages of education and research. However, it is the most relevant for the following three programmes: - Public Health and Epidemiology - Biostatistics and Bioinformatics - Clinical Research Language The course will be held in English. Form A mix of lectures, case studies, and roundtable discussions. 2.8 ECTS Course director Tibor V. Varga (director) He/him Associate Professor Copenhagen Health Complexity Center, Department of Public Health, University of Copenhagen tibor.varga@sund.ku.dk Denise Utochkin (director) They/them Postdoc Copenhagen Health Complexity Center, Department of Public Health, University of Copenhagen denise.utochkin@sund.ku.dk Course coordinator Inchuen Huynh She/her PhD student Copenhagen Health Complexity Center, Department of Public Health, University of Copenhagen inchuen.huynh@sund.ku.dk Teachers When considering external teachers, special attention was paid to gender equity. Faculty Teachers Denise Utochkin (they/them), Postdoc – University of Copenhagen, Denmark Inchuen Huynh (she/her), PhD student – University of Copenhagen, Denmark Fernando Racimo (he/him), Associate Professor – University of Copenhagen, Denmark External Teachers (the list is tentative): Nanna Inie (she/her), Postdoc – IT University of Copenhagen, Denmark Fieke Jansen (she/her), Postdoc – Critical Infrastructure Lab, University of Amsterdam, Netherlands Émile P. Torres (they/them), Postdoc – Case Western Reserve University, US Dan McMillan (he/him), Lecturer – Department of Computing, Goldsmiths, University of London, UK Ericka Johnson (she/her), Professor – Linköping University, Sweden Dates 2 December 2024 (Monday) - Impact of AI on Labour, The Environment, Democracy, Science, and Creativity (3 hours) - History of Statistics, Cybernetics, and AI (3 hours) 3 December 2024 (Tuesday) - Introduction to Critical Algorithm Studies & Digital Science and Technology Studies (4 hours) - History of the Luddite Movement (1 hour) - Administrative Violence, Accounting Realism, and Small Acts of Defiance – exercise (1 hour) 4 December 2024 (Wednesday) - Under the hood of ‘AI’: Stochastic Parrots and Mechanical Turks (1 hour) - Techno-solutionism, green capitalism, and colonialism: ‘optimising our way out of the climate crisis’ (1 hour) - Introduction to Critical Data Studies (1 hour) - Degrowth & solarpunk (1 hour) - Resisting AI (1 hour) - Ideology behind AI: from eugenics to the ‘good’ kind of human extinction (1 hour) 5 December 2024 (Thursday) - The Ones Who Walk away from Omelas – exercise (2 hour) - Imagination, Agency, and the Responsibility of the University (1 hour) - Course feedback (30 minutes) - Reclaiming utopia – exercise (2 hour) - Closing remarks (30 minutes) Course location TBA Registration Please register before November 4. Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrollment. Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

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