AI and Data Ethics. From Data (Feminism|Gaps|Work) to Critical (Archival|Data|Code) Studies

Course Overview

AI and data ethics have become increasingly important. With the rise of generative artificial intelligence, which is becoming embedded in more and more aspects of everyday life, even people who had not previously thought about AI or actively used it are now affected by these technologies. As a result, public awareness is increasing. On the one hand, there is the technophilia of the tech industry, which often makes grand claims about future capabilities or equally fuels the surrounding hype through doomerist projections. On the other hand, there are growing critical responses from academia, activist communities, and younger generations working both within and alongside technology companies. It is therefore high time to discuss AI ethics and make it part of curricula so that students can learn to critically engage with these technologies.

Students will learn how these technologies work, how knowledge is encoded, and how they are produced materially in order to develop a meaningful understanding of AI and data ethics.

Data ethics is particularly important because AI and other computational methods fundamentally rely on data. The quality of their outputs and their functioning depend not only on the data itself, but also on the labour, practices, and politics involved in creating that data. AI ethics therefore cannot be separated from data ethics. Fortunately, the humanities already provide many relevant discourses and forms of expertise that can inform these discussions, including critical archival studies, critical data studies, critical code studies, and more recent interventions such as the Data Feminism and the Data Feminism for AI manifestos. Other important contributions come from activist groups conducting algorithmic audits and from proposals to adopt documentation practices such as Datasheets for datasets. Within the digital humanities, scholars are increasingly addressing issues such as the gender data gap, although this is only one facet of broader structural inequalities. More generally, discussions around datasets and data work increasingly address the labour and exploitative working conditions involved in creating training data and the environmental dimensions of this industry. This class will therefore not only introduce students to how AI and data-driven technologies function, but also help them critically engage with them. This involves not only understanding the technologies themselves, but also drawing on the existing activist and humanities-based discourses surrounding them. After completing the class, students should be able to engage with and apply these perspectives critically.

Students will also learn that ethics is not a monolithic field, but one shaped by many disciplinary traditions. For example, some approaches to ethics emerge from computer science, others from philosophy, and still others from the intersection of both (such as Explainable AI). However, these are not the only possible approaches to computer ethics. Other traditions, such as cultural heritage ethics and feminist ethics, also offer invaluable perspectives.

Students will therefore gain a broad understanding of critical AI approaches while also learning why such approaches are necessary, how these technologies function, and how related issues can be addressed in both theoretical and practical ways.

This course is taught at Humboldt-Universität zu Berlin (Winter Semester 2026/2027) and Freie Universität Berlin (Summer Semester 2027).