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. 

The course specifically addresses the following topics:

Week 1 - Introductory concepts: what are data? 

Week 2 -Introductory concepts: what are data infrastructures?

Week 3 - The politics of artefacts and algorithmic governance

Week 4 - Data governance in security and migration 

Week 5 - Data governance and climate changes

 

Prerequisites: Basic, non-technical knowledge of what a database, a categorical system, a dataset, statistical analysis, and an algorithm are.

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 - Introductory concepts: what are data?

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

Dourish, P. (2017). “No SQL: The Shifting Materialities of Database Technology”. In Dourish, P., The Stuff of Bits: An essay on the materialities of information. Cambridge, MA: The MIT Press.

Recommended:

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

 

Week 2 - Introductory concepts: what are data infrastructures?

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.

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

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

Barry, A. (2020). The material politics of infrastructure. In TechnoScienceSociety: Technological reconfigurations of science and society. Cham: Springer International Publishing: pp. 91-109.

Week 3 - The politics of artefacts and algorithmic governance

Winner, Langdon 1980. “Do Artifacts Have Politics?” Daedalus 109(1), 121-136.

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 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.

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. 

For attending students only: 5% of final evaluation through class participation (oral or via pad).

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 2026/2027 academic year, final assignment delivery is  scheduled in the following months:

- December 2026 for all students (via Virtuale)

- January 2027 for all students(via Virtuale)

- April 2027 for all students (via email)

- June 2027 for all students (via email)

- September 2027 for students who did not pass (via email)

- November 2027 for students who did not pass (via email)

 

Guidelines for Using GenIA

When used with awareness of its working, AI can be a tool to support individual learning and self-assessment. Regarding learning assessment (e.g., exams), limited, declared, and non-substantial use of AI is only allowed for support activities (editing, rephrasing). Substantial use to pursue the analytical goals of the exam is not allowed. Declaration should specify the GenAI model used and the prompts.

 

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