B8911 - Data Infrastructures and Algorithmic Governance (1) (LM)

Learning outcomes

The course aims to familiarize students with past and contemporary developments in studies on information infrastructures, digital Science and Technology Studies, critical data and algorithms studies. Notably, the course focuses on the implications of data production, curation, circulation and validation for the governance of topical and/or contested areas, such as security, health, land management and climate change, migration.

Course contents

The course addresses the main theoretical and empirical debates on contemporary developments concerning data infrastructures, algorithms and automated knowledge production (including but not limited to AI), with particular attention to their agency and therefore to aspects related to governance and power.

In doing so, the course mobilizes the perspectives of the social studies of technology (STS), information infrastructure studies, critical data studies and critical studies on algorithms and AI. These approaches are learned through careful communal readings of the main texts that address the international debate, the support of practical exercises in class, as well as analyses of empirical cases.

During the course, interactive moments are dedicated to group exercises, commentary on texts, analysis of case studies concerning issues that are the subject of public debate such as security, the digital management of migrant populations, climate change, state building.

No prior technological knowledge is required, but an interest in learning emerging trans-disciplinary skills.

The course specifically addresses the following topics:

Week 1 -Genealogies: From large-scale systems to infrastructures and AI

Week 2 -Information infrastructures in STS and critical data studies

Week 3 -Infrastructures, governance and statecraft

Week 4 - Data governance in security and migration 

Week 5 - Data governance and climate changes

Readings/Bibliography

The syllabus is mandatory for both attending and non attending students, and is made of journal articles and book chapters. They are all available on Virtuale or through the UniBO digital library (AlmaRE). See Virtuale for syllabus details and copies of the material.

Students should read the mandatory texts, and are suggested to read also the recommended ones, which nevertheless will not be object of final evaluation.

Week 1 -Genealogies: From large-scale systems to infrastructures and AI

Edwards, P. et al. (2025) “Infrastructures,” in U. Felt and A. Irwin (Eds.) Encyclopedia of Science & Technology Studies. Edward Elgar.

Hughes, T. P. (1987) “The evolution of large technological systems,” in W.E. Bijker, T.P. Hughes, and T. Pinch (Eds.) The social construction of technological systems: New directions in the sociology and history of technology. Cambridge, MA: The MIT Press, pp.51-82.

Recommended:

Collins, H.M. (1987) “Expert systems and the science of knowledge,” in W.E. Bijker, T.P. Hughes, and T. Pinch (Eds.) The social construction of technological systems: New directions in the sociology and history of technology. Cambridge, MA: The MIT Press, pp.329-349.

Week 2 -Information infrastructures in STS and critical data studies

Bowker, G. C., & Star, S. L. (1999) Sorting Things Out: Classification and Its Consequences. Cambridge, MA: The MIT Press, pp. 1-50.

Star, SL and Ruhleder K (1996) “Steps Toward an Ecology of Infrastructure: Design and Access for Large Information Spaces.” Information Systems Research 7(1): 111–34.

Gitelman, L. and Jackson, V (2013) “Introduction,” in Gitelman L. Raw data is an Oxymoron. Cambridge, MA: The MIT Press, pp. 1-14.

Dalton, Craig & Jim Thatcher (2014) “What Does A Critical Data Studies Look Like, And Why Do We Care?,” Society & Space. Available at https://www.societyandspace.org/articles/what-does-a-critical-data-studies-look-like-and-why-do-we-care

Recommended:

Crawford, Kate (2021) “Classification,” in The Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press, pp. 123-149.

Kitchin, R. (2014) The Data Revolution. Big data, open data, data infrastructures and their consequences. London: Sage.

Week 3 -Infrastructures, governance and statecraft

Mitchell, Timothy. 1991. “The Limits of the State: Beyond Statist Approaches and Their Critics.” The American Political Science Review 85 (1): 77-96.

Mukerji, Chandra. 2011. “Jurisdiction, Inscription, and State Formation. Administrative Modernism and Knowledge Regimes.” Theory and Society 40 (3): 223-45. doi: 10.1007/s11186-011-9141-9.

Pelizza, Annalisa. 2016a. “Disciplining Change, Displacing Frictions. Two Structural Dimensions of Digital Circulation across Land Registry Database Integration.” Tecnoscienza Italian Journal of Science & Technology Studies 7 (2): 35-60.

Recommended:

Pelizza, Annalisa 2016b. “Developing the Vectorial Glance: Infrastructural inversion for the new agenda on governmental information systems.” Science, Technology and Human Values 41 (2): 298-321. https://doi.org/10.1177/0162243915597478

Schipper, Frank, and Johan Schot. 2011. “Infrastructural Europeanism, or the Project of Building Europe on Infrastructures. An Introduction.” History and Technology 27 (3): 245-64. doi: 10.1080/07341512.2011.604166.

Week 4 - Data governance in security and migration

Aradau, C., & Blanke, T. (2022) Algorithmic reason: The new government of self and other. Oxford: Oxford University Press, pp. 1-66.

Pelizza, A. (2021) “Identification as translation: The art of choosing the right spokespersons at the securitized border.” Social Studies of Science 51 (4), 487-511. https://doi.org/10.1177/0306312720983932

Recommended:

Aradau, C., & Blanke, T. (2022) Algorithmic reason: The new government of self and other. Oxford: Oxford University Press, pp. 68-90.

Week 5 - Data governance and climate changes

Edwards, P. N. (2013). A vast machine: Computer models, climate data, and the politics of global warming. Cambridge, MA: The MIT Press, pp. XIII-XXIV; 1-59; 83-110.

Recommended:

Edwards, P. N. (2013). A vast machine: Computer models, climate data, and the politics of global warming. Cambridge, MA: The MIT Press, pp. 338-355.

Teaching methods

The teaching style favors interactivity. Classes include lectures by the teacher, presentations by students, exercises and discussions in the classroom.

The purpose of the activities in the classroom is threefold: 1) to support and develop students' understanding of the literature; 2) to support and develop their analytical and research skills before the formal assessment; 3) to promote peer learning.

To achieve these objectives, the drafts of the assignments to be submitted for final assessment will be preliminarily discussed collegially in class (see assessment methods).

Assessment methods

The learning process is assessed through an analytical assignment of a case study chosen by the students at the end of the course.This works for both attending and non-attending students.

The grade is expressed as 30/30. The maximum score achievable is therefore 30 cum laude. The exam is considered passed with a minimum score of 18/30.

During the 2025/2016 academic year, final assignment delivery is  scheduled in the following months:

- December 2025 for all students

- January 2026 for all students

- April 2026 for all students

- July 2026 for students who did not pass

- September 2026 for students who did not pass

- November 2026 for students who did not pass

Students with Specific Learning Disorders (SLD) or temporary or permanent disabilities:

it is recommended to contact the relevant University office in advance (https://site.unibo.it/studenti-con-disabilita-e-dsa/it ). The office will be responsible for proposing any necessary adjustments to the interested students. These adjustments must, however, be submitted at least 15 days in advance for approval by the instructor, who will assess their appropriateness in relation to the learning objectives of the course.

Teaching tools

Classes are held in presence: in the classroom, computer with video projector and digital whiteboard. The teaching material consists of texts, lessons prepared by the teacher and case studies.

The syllabus, when not protected by copyright, is made available to students through the Virtuale teaching platform (http://virtuale.unibo.it) of the University of Bologna. Copyrighted texts over a certain number of pages are available at the library system of the University of Bologna.

Office hours

See the website of Annalisa Pelizza