Introductory description

This module will equip students for the critical and creative analysis of contemporary impacts of computational technology including AI on society and culture by introducing them to relevant classic and current perspectives from the social sciences and humanities on the interaction between technology and society.

Students will learn about these perspectives and how to apply them to key contemporary innovations across data, digital and AI technologies through a review of key approaches, relevant historical case studies of science, technology & society interaction and project-based learning focused on timely public, policy and cultural debates and incidents involving contemporary technologies.

The module consists of three parts: 1) lectures introducing key perspectives on Tech & Society 2) presentations of classic case studies and debates 3) project-based work guided by assignments. Sessions during all three parts will be supported by tutor-led seminars.

The first, lecture-based part of the module (w1-3) will offer an introduction to relevant classic and contemporary perspectives from the social sciences and humanities on the interaction between technology and society 1) the rise of technological societies; 2) the invention of bureaucracy and the automation of the state; 3) feminist and ecological perspectives on technology. These lectures will also introduce students to core heuristic concepts: technological determinism, informational capitalism and the socio-technical.

In the second part (w4-6), the module will present exemplary historical cases of the interaction between technology and society: nuclear power, expert systems (symbolic AI), and digital surveillance. The presentation of these historical cases will also enable the further exploration of key concepts for understanding the interaction between Technology and Society during the 20th and early 21st Century, such as the innovation state, situated practices, and the data economy.

In the third part and final of the module (w7-9), students will be introduced to contemporary social and cultural cases involving tech with a special focus on AI. Students will explore these cases through project-based work guided by assignments. They will draw on the concepts and cases previously introduced in order to create a case study of a contemporary technology and its interaction with or impact on society.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

PART 1: Key concepts
Week 1: Tech & Society Now
How do new technologies transform society and what agency do communities, publics and citizens have to influence this process?

Week 2: Automating society
How is automation transforming social and public life and how does it change the relations between technology, economy and society?

Week 3: The socio-technical
While technology may change society, technology cannot function without human effort and human labour. How then should we understand the relation between technology and society?

PART 2: Historical cases and debates
Week 4: Nuclear power: challenges of the innovation state
During the 1970s and 1980s, the introduction of nuclear technologies led to extensive debate and the creation of Science & Society movements. What consequences did this have for the relations between innovation, state and society?

Week 5: Expert systems: the social life of intelligent machines
In the 1980s and 1990s, the creation of a new type of expert systems led to a wave of excitement and concern about intelligent machines and their role in social life. What were the core issues identified in these debates?

Week 7: Digital surveillance: evaluating digital technology in practice
During the 2010s, new AI-based technologies such as predictive analytics and biometrics were taken up by law enforcement and other public agencies. This led to extensive societal debate about the end of privacy and the accountability of the police. How did social research contribute to the understanding of the issues?

PART 3: Contemporary studies of tech & Society

Week 8: Introduction to contemporary cases
For each of the following cases, students will be presented with a dossier with relevant materials (scientific articles, media reports, online resources). Working in small groups, they will examine Tech & Society issues for each of these cases guided by assignments. Cases will change year on year, the following is an indicative list:
Intelligent Assistants
Future of Work
Biometrics
Public Compute
Future of Privacy

Week 9: Group work on case studies
Guided by assignments, students will work in small groups using data mapping techniques to create case studies. They will explore, map and visualise core Tech & Society issues raised by their cases.

Week 10: Grand finale: connecting concepts and cases
During the last session, students will present their case studies of Tech & Society. Through a guided discussion, students will explore the wider historical, social and political context of their cases, and make connections with key readings introduced in the first half of the course.

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate a conceptual understanding of the challenges that technological innovation poses to society and key ideas from social science and humanities to understand these challenges
• Evaluate historical cases of socio-technical change and the debates they gave rise to about the impact of technological innovation on society.
• Demonstrate a case-based understanding of contemporary instances of socio-technical change and the role interdisciplinary social research can play to inform public debate and policy
• Develop an appreciation of innovative forms of inquiry, policymaking, intervention and engagement at the intersection of technological innovation and society
• Demonstrate the ability to use methods of issue mapping to create case studies to evaluate the interaction between contemporary technology and society.

Indicative reading list

Week 1
Key reading
Rettberg, J. W., Crawford, K., Schultz, J., Taylor, L., Suchman, L., Andrejevic, M., & Ananny, M. (2024). An AI Society: Artificial intelligence is reshaping society, but human forces shape AI. Social scientists and humanities experts explore how to harness the interaction, revealing urgent avenues for research and policy. Issues in Science & Technology, 40(2). https://issues.org/an-ai-society/
Further readings
Aradau, C., & Blanke, T. (2022). Algorithmic reason: The new government of self and other (p. 288). Oxford University Press
Adorno, T. (1987) Late Capitalism or Industrial society?, Address to the 16th Congress of the German Sociological Association, 1968. Reprinted in Suhrkamp, 1972) pp. 354-370
Beck, U. (1986) Risk society: Towards a New Modernity, London and New Dehli: Sage
Bijker, W. E., & Law, J. (Eds.). (1994). Shaping technology/building society: Studies in sociotechnical change. MIT press.
Bell, D. (1976) The coming of post-industrial society. New York: Basic books
Feenberg, A. (2010). Ten paradoxes of technology. Techné: Research in Philosophy and Technology, 14(1), 3-15.
Kling, R. (Ed.). (1996). Computerization and controversy: Value conflicts and social choices. Elsevier.
Latour, B. (1996). Aramis, or the Love of Technology. Cambridge: Harvard University Press.
Polanyi, K. (1944/2024). The great transformation. London: Penguin
Marx, K. and F. Engels, The communist manifesto (excerpt)
Rouvroy, A., Berns, T., & Carey-Libbrecht, L. (2013). Algorithmic governmentality and prospects of emancipation. Réseaux, 177(1), 163-196.
Verbeek, P. P. (2006). Materializing morality: Design ethics and technological mediation. Science, Technology, & Human Values, 31(3), 361-380.
Wyatt, S. (2008). Technological determinism is dead; long live technological determinism. The handbook of science and technology studies, 3, 165-180.

Week 2
Key reading
Fourcade, M., & Healy, K. (2024). The ordinal society. Harvard University Press (excerpt)
Further reading
Cardon, D., & John-Mathews, J. M. (2023). The displacement of reality tests. The selection of individuals in the age of machine learning. Distinktion: Journal of Social Theory, 24(2), 217-240.
Edwards, P. N. (1996). The closed world: Computers and the politics of discourse in Cold War America. Cambridge: MIT press.
Esposito, E. (2017). Artificial communication? The production of contingency by algorithms. Zeitschrift für Soziologie, 46(4), 249-265.
Fourcade, M., & Healy, K. (2017). Seeing like a market. Socio-economic review, 15(1), 9-29.
Lynch, M. (1997). A sociology of knowledge machine. Ethnographic Studies, 2, 16-38.
Ranchordas, S., & Scarcella, L. (2021). Automated government for vulnerable citizens: Intermediating rights. Wm. & Mary Bill Rts. J., 30, 373.
Star, S. L. (1990). Power, technology and the phenomenology of conventions: on being allergic to onions. The Sociological Review, 38(1_suppl), 26-56.
Weber, M., (1921/2019) Economy and Society, Cambridge: Harvard University Press

Week 3
Key reading
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019, January). Fairness and abstraction in sociotechnical systems. In Proceedings of the conference on fairness, accountability, and transparency (pp. 59-68).
Further reading
Faulkner-Gurstein, R., & Wyatt, D. (2023). Platform NHS: reconfiguring a public service in the age of digital capitalism. Science, Technology, & Human Values, 48(4), 888-908.
Haraway, D. (2013). A cyborg manifesto: Science, technology, and socialist-feminism in the late twentieth century. In The transgender studies reader (pp. 103-118). Routledge.
Martin, A., Myers, N., & Viseu, A. (2015). The politics of care in technoscience. Social studies of science, 45(5), 625-641.
Star, S. L. (1999). The ethnography of infrastructure. American behavioral scientist, 43(3), 377-391.
Suchman, L. (2002). Located accountabilities in technology production. Scandinavian journal of information systems, 14(2), 7.
Suchman, L. A. (2007). Human-machine reconfigurations: Plans and situated actions. Cambridge: Cambridge university press.
Schwarz-Cohen, R. (1985) More Work For Mother: The Ironies Of Household Technology From The Open Hearth To The Microwave, Basic Books.
Wajcman, J., & Young, E. (2023). Feminism Confronts AI. Feminist AI: Critical Perspectives on Algorithms, Data, and Intelligent Machines, 47.

Week 4
Key reading
Esselborn, S., & Zachmann, K. (2023). Evidence against the “Nuclear State”: Contesting Technoscience through Gegenwissenschaft in the 1970s and 1980s. In Evidence Contestation (pp. 193-223). Routledge.
Further reading
Callon, M. (2003). The increasing involvement of concerned groups in R&D policies: what lessons for public powers. Science and innovation: Rethinking the rationales for funding and governance, 30-68.
Callon, M., Lascoumes, P., & Barthe, Y. (2011). Acting in an uncertain world: An essay on technical democracy. MIT press.
Hecht, G. (2009). The radiance of France. Cambridge, MA: MIT press
Jasanoff, S., & Kim, S. H. (2009). Containing the atom: Sociotechnical imaginaries and nuclear power in the United States and South Korea. Minerva, 47, 119-146.
Roseneil, S. (1995). Disarming patriarchy: Feminism and political action at Greenham. Milton Keynes: Open University Press
Wynne, B. (1996). May the sheep safely graze? A reflexive view of the expert-lay knowledge divide. Risk, Environment and Modernity: Towards a New Ecology, Thousand Oaks: Sage Publications

Week 5
Key Reading
Collins, H. (2018). Artifictional intelligence: against humanity's surrender to computers. John Wiley & Sons.
Further reading
Adam, A. (2006). Artificial knowing: Gender and the thinking machine. Routledge.
Agre, P. E., & Chapman, D. (1990). What are plans for?. Robotics and autonomous systems, 6(1-2), 17-34.
Cardon, D., Cointet, J. P., & Mazières, A. (2018). La revanche des neurones. Réseaux, 211(5), 173-220.
Cicourel, A. V. (1986). 10 Social Measurement as the Creation of Expert Systems.Metatheory in social science: Pluralisms and subjectivities, 246.
Dreyfus, H. L. (1992). What Computers Still Can’t Do: A Critique of Artificial Reason. The MIT Press.
Dreyfus, H. L., & Dreyfus, S. E. (1992). What artificial experts can and cannot do. AI & society, 6, 18-26.
Collins, H. M. (1990). Artificial experts: Social knowledge and intelligent machines. Cambridge: MIT press.
Marres, N., & Sormani, P. (2023). Testing ‘AI’: do we have a situation?. University of Siegen, working paper.
Raymond, E. (1999). The cathedral and the bazaar. Knowledge, Technology & Policy, 12(3), 23-49.
Suchman, L. (2008). Feminist STS and the Sciences of the Artificial. The handbook of science and technology studies, 3, 139-164.

Week 7
Key reading
Brayne, S. (2017). Big data surveillance: The case of policing. American sociological review, 82(5), 977-1008.
Further reading
Clarke, R. (1988). Information technology and dataveillance. Communications of the ACM, 31(5), 498-512.
Faulkner-Gurstein, R., & Wyatt, D. (2023). Platform NHS: reconfiguring a public service in the age of digital capitalism. Science, Technology, & Human Values, 48(4), 888-908.
Foucault, M. (1975) Discipline and power. London: Penguin
Fussey, P., Davies, B., & Innes, M. (2021). ‘Assisted’facial recognition and the reinvention of suspicion and discretion in digital policing. The British journal of criminology, 61(2), 325-344.
Hintz, A., & Dencik, L. (2016). The politics of surveillance policy: UK regulatory dynamics after Snowden. Internet Policy Review.
Lyon, D. (2007). Surveillance studies: An overview. Cambridge: Polity Press
Marres, N. (2021). What is sociological about the digital society? An Introduction to Sociology, J Solomos, S. Neale, and K. Murji (Eds). London and New York: Sage.
Van der Velden, L. (2015). Leaky apps and data shots: Technologies of leakage and insertion in NSA-surveillance. Surveillance & Society, 13(2), 182-196.
Zuboff, S. (2018). The age of surveillance capitalism. New York: Profile books.

Week 8
Indicative resources
https://www.adalovelaceinstitute.org/our-work/projects/
https://datasociety.net/research/
https://ico.org.uk/about-the-ico/research-reports-impact-and-evaluation/research-and-reports/technology-and-innovation/biometrics-technologies/

Research element

The last part of the module consists of a small research project guided my assignments in which students will learn to mobilise academic literature to analyse and communicate contemporary issues in Tech & Society.

Interdisciplinary

The module presents a body of knowledge from sociology, science & technology studies, history, political theory, and digital media studies and enables students to draw on this knowledge to analyse contemporary public affairs in the area of tech and society.

International

Case studies will be selected from the UK as well as international contexts

Subject specific skills

• conceptual skills: the ability to use high level concepts to interpret instances and consequences of Tech & Society interactions.
• analytic skills: the ability to interpret case studies and associated literature to understand historical and contemporary relations and dynamics between Tech and Society.
  empirical skills : the ability to analyse quantitative and qualitative data to detect and describe meaningful patterns in Tech and Society creative skills: using visual and spatial forms to represent Tech and Society interactions and their patterns .

Transferable skills

-communicative skills : the ability to commmunicate about complex situations in persuasive ways with a variety of stakeholders.

• collaborative skills: the ability to work effectively in teams to deliver short projects and presentations.
• synthesis skills : the ability to integrate ideas from academic and grey literatures with understandings of real-world incidents.
• creative skills: the ability to deploy visual and related aesthetic formats to engage diverse audiences.