02.181HT AI & Society

Wherever we look, artificial intelligence (AI) becomes increasingly part of our social lives. Whether in physical or digital shape, AI seems to gradually entangle human practices, social structures, and even our expectations for our future world as human beings. At the same time, the social implications of AI are incrementally coming into view: the work of designers and data workers, the real-world settings in which systems operate, the cultural imaginaries attached to “intelligent” technologies, and the everyday encounters people have with automated systems are only a few of the most urgent issues that emerge. But how precisely is AI related to society, and how can we make sense of this relationship in actual AI design? From a social science perspective, this course teaches students to understand AI as a socio-technical system – that is, as something produced through human labour, organisational routines, cultural assumptions, power relations, governance and everyday practices – not just code and models. It provides a fundamental basis for understanding AI solutions in a socially conscious manner, seeking to answer this and other pressing questions of our time: What forms of human labour and expertise are required to produce and sustain AI systems? How do people make sense of, resist, or accommodate AI in their daily lives? And how can engineers consider the political and ethical questions that accompany AI’s increasing influence?

In the course, students will learn about the relationship between AI and society and become familiar with analytical tools to understand various aspects that shape how AI systems are imagined, built, implemented, maintained and lived with in real-world contexts. You will become acquainted with foundational social science perspectives on AI and society, engage with empirical studies, and learn methodological tools for studying AI “in the wild.” Across a multi-disciplinary set of perspectives – from science and technology studies (STS) to critical data studies – you will learn to situate AI within broader social relations, labour structures, cultural imaginaries, and power dynamics that shape its development and use. The course offers essential preparation for engineers who wish to design AI technologies responsibly, attend to their social context, and contribute to sustainable and socially aware innovation. Structurally, the course is constructed to complement students’ technical education by providing a foundation for critically and constructively engaging with AI as a socio-technical system, enabling students to approach design and engineering work with greater societal awareness. The examination includes active participation in weekly seminars, prepared oral presentations, peer-feedback and a final paper. Through this work, the students will demonstrate how the social perspectives taught in this course can support novel and responsible ways of thinking about, implementing, and evaluating AI technologies.

The main objective of the course is to equip students with relevant social perspectives and analytical tools to understand the relationship between AI technologies and society, and to cultivate a critical awareness of how AI technologies shape and are shaped by social, cultural, ethical, and organisational contexts. As such, the course is designed to support students in developing responsible and socially aware approaches to AI design and implementation. To do so, it combines theoretical perspectives with methodological training to enable students to examine AI systems in their real-world contexts as complex socio-technical arrangements. Through seminar-based learning, collaborative discussion, and iterative feedback, the course encourages students to develop reflective, ethically informed, and socially engaged approaches to AI innovation.

After the course, the student will be able to:

Week 1: Introduction: AI & Society

In the introductory session, the content, motivation, and goals of the course will be presented. We will begin by discussing how AI has become a central topic of societal concern, and how social sciences contribute to understanding AI beyond its technical components. The session will briefly examine the role of theory in studying AI as a socio-technical system and introduce key questions that will guide the remainder of the course. Students will also be introduced to the seminar format, course assignments, and expectations for participation. Finally, a brief orientation will be given on how to approach and summarize academic readings, supporting students in developing the analytical skills required for the weeks ahead.

Week 2: What Is “AI” in our society?

In this session, we examine one of the most foundational questions of AI: What do we actually mean when we talk about “AI”? Although AI is frequently treated as a coherent technical object, the readings for this week challenge this assumption by exploring how AI is defined, enacted, and made meaningful across different social, political, and organizational settings. We will discuss how various actors — from policymakers and engineers to journalists and AI practitioners — participate in crafting the narratives, imaginaries, and expectations that stabilize AI as a recognisable phenomenon. We will also explore alternative approaches to thinking about AI.

Week 3: The Rise of AI Ethical Principlism

Following the previous session’s exploration of how AI is defined and enacted, this week turns to the rapidly expanding field of AI ethics and its growing influence on policy, governance, and design. We begin by examining the emergence of ethical principles, frameworks, and toolkits that aim to guide responsible AI development. We discuss dominant principles such as fairness, accountability, transparency, and human oversight, while mapping the global landscape of guidelines and the efforts to translate abstract principles into organizational practices. Building on this, the session introduces critical perspectives that question the limitations of principle-based approaches, such as the tendency to be deployed as legitimizing means that sometimes intentionally obscure hidden power relations of AI as broader socio-technical assemblages. By the end of the session, students should be able to understand both the promises and limitations of AI ethical frameworks, and contrast them with more situated, practice-oriented, and experiential approaches to addressing the real-world implications of AI systems.

Week 4: AI in-the-making

In this session, we shift our attention from abstract discussions of AI to the practical activities through which AI systems are made, adjusted, negotiated, and justified in everyday work. Drawing on ethnographic studies, the readings explore the complexities that occur in situated practice as AI is developed, involving experimentation, coordination, uncertainty, and ethical judgment. The focus of the week is therefore on how AI actually comes into being through the hands and decisions of designers, engineers, managers, and institutional stakeholders. The session also addresses who shapes what becomes visible or invisible in AI design. The goal is for students to recognize the importance of understanding AI as a process rather than a finished product.

Seminar A Research proposal draft deadline.

Week 5: Power in AI design

Following from the previous session’s focus on the situated practices through which AI systems are made, this week delves deeper into the concept of power within AI design. We particularly zoom into how designers configure users, embed assumptions into technical choices, and shape what forms of agency, responsibility, and action become possible – or impossible – through AI systems. We also explore how narratives of technical inevitability and determinism can privilege certain institutional or expert perspectives over others. By tracing how power operates through seemingly mundane design decisions, the session provides students with analytical tools to recognize how AI systems both reflect and reinforce social hierarchies, and why questions of responsibility and control are central to understanding AI in practice. It also allows students to develop the analytical tools needed to trace how values, assumptions, and practical constraints become embedded in AI systems as they are made.

Supervision Seminar A. Assignment and feedback session.

Week 6: Society in AI

In this session, we further our interest from the prior session that explored power in AI design to interrogate in more detail how AI systems build on existing social, economic, and organizational structures. Rather than treating AI as an autonomous technological achievement, the readings emphasize the existing societal biases made available within data infrastructures that underpin algorithmic systems. The discussion will address how “society in AI” becomes visible through the crafting of ground truths, the coordination of distributed microwork, and the institutionalization of data as a form of capital. By the end of the session, students should be able to understand how AI technologies are inseparable from the social arrangements that make them possible, and how these arrangements, in turn, shape the functioning, limits, and consequences of AI in real-world contexts.

Week 7: Recess Week

Week 8: Maintaining AI

Building on the previous session’s focus on the social arrangements that make AI possible, this week turns to the often-overlooked practices of maintenance, repair, and care that sustain AI systems once they are deployed. The readings draw attention to the continuous human effort required to handle breakdowns, recalibrate models, manage exceptions, and keep systems aligned with organizational needs and ethical expectations. Whether through the embodied care surrounding social robots, the epistemic labor that makes algorithmic outputs reliable, or the negotiated role relationships between humans and algorithmic “co-workers,” this session foregrounds how AI technologies depend on ongoing interpretive, moral, and material work. By examining these maintenance practices, students will gain insight into AI as a dynamic and fragile socio-technical arrangement, whose stability relies on forms of labor and judgment that are rarely acknowledged in mainstream narratives of automation.

Week 9: AI in society

In this session, we turn to the broader societal arrangements within which AI systems operate, asking how algorithmic technologies reshape institutions, infrastructures, and forms of social coordination. The readings foreground how AI becomes embedded in existing social orders, highlighting the ways machine learning interacts with governance structures, market dynamics, and everyday norms. Through empirical cases ranging from self-driving cars to automated trading systems, we examine how the integration of AI into society requires new forms of learning, negotiation, and oversight. By tracing these developments, students will gain a deeper understanding of how AI does not simply enter society as a neutral technological tool, but participates in reorganizing social relations, expectations, and forms of governance. The session prepares students to critically engage with the broader social implications of AI as it becomes integrated into everyday life.

Week 10: Human encounters with AI

Building on the previous session’s focus on how AI shapes broader social arrangements, this week turns more closely to the immediate, lived encounters between humans and AI systems. Through diverse empirical examples, we explore how people interpret, negotiate, and respond to AI in everyday settings. By situating AI within lived experiences, this session highlights the importance of understanding AI not as a distant or opaque system but as something encountered, interpreted, and sometimes resisted in everyday life. Through these discussions, students will become familiar with analytical tools to study the subtle, often overlooked ways that AI becomes part of human social worlds.

Seminar B Research proposal draft deadline.

Week 11: Supervision Seminar B

In this week, we will be dealing with your work and research interests in-depth. Be prepared to have read each other’s research proposals and provide in-depth feedback. Feedback will be assessed based on depth, analytical engagement, constructiveness, self-reflexivity, and critical thinking.

Week 12: AI as culture

Following our previous weeks’ foci on how people encounter and negotiate AI in everyday life, this session shifts attention toward the methodological and conceptual tools needed to study such encounters in practice. Treating algorithms as cultural artifacts rather than purely technical systems, the readings introduce ethnographic and qualitative approaches that allow you to trace how algorithms are embedded in social worlds, shaped by organizational routines, and interpreted by different actors. We discuss strategies for accessing algorithmic settings, the challenges of studying opaque systems, and the analytic value of grounded theory and interviewing for making sense of socio-technical complexity. This session thus provides students with a methodological foundation for investigating AI “in the wild,” preparing them for the empirical and conceptual work required in their research proposals.

Week 13: AI Futures

As a concluding session, this week turns our attention toward emerging and speculative trajectories of AI, examining how future-oriented imaginaries, embodied experiences, and evolving social practices shape what AI may become. The readings explore questions of data legacies, erasure, and traceability in large-scale machine learning, highlight the sensory and embodied forms of coordination that arise in highly automated environments such as robotic surgery, and investigate the ambiguous agencies and unexpected frictions that service robots generate in everyday encounters. Together, these perspectives encourage students to consider how AI futures are not predetermined by technical progress but are continuously negotiated through political choices, professional practices, and human experiences. This session thus invites students to reflect critically on the longer-term ethical, social, and cultural implications of AI, helping to formulate nuanced and context-sensitive research proposals.

Research Proposal Assignment deadline

Björn Fischer